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Summary: In this paper, we study a UAV‐based fog or edge computing network in which UAVs and fog/edge nodes work together intelligently to provide numerous benefits in reduced latency, data offloading, storage, coverage, high throughput, fast computation, and rapid responses. In an existing UAV‐based computing network, the users send continuous requests to offload their data from the ground users to UAV–fog nodes and vice versa, which causes high congestion in the whole network. However, the UAV‐based networks for real‐time applications require low‐latency networks during the offloading of large volumes of data. Thus, the QoS is compromised in such networks when communicating in real‐time emergencies. To handle this problem, we aim to minimize the latency during offloading large amounts of data, take less computing time, and provide better throughput. First, this paper proposed the four‐tier architecture of the UAVs–fog collaborative network in which local UAVs and UAV–fog nodes do smart task offloading with low latency. In this network, the UAVs act as a fog server to compute data with the collaboration of local UAVs and offload their data efficiently to the ground devices. Next, we considered the Q‐learning Markov decision process (QLMDP) based on the optimal path to handle the massive data requests from ground devices and optimize the overall delay in the UAV‐based fog computing network. The simulation results show that this proposed collaborative network achieves high throughput, reduces average latency up to 0.2, and takes less computing time compared with UAV‐based networks and UAV‐based MEC networks; thus, it can achieve high QoS. [ABSTRACT FROM AUTHOR]

Abstract Traffic congestion is one of the major challenges faced by daily commuters in smart cities. An autonomous transportation system with a 5 G-based Cellular Vehicle-to-Everything (C-V2X) communication system is the solution to meet the traffic challenges faced in smart cities. Vehicular networks provide wireless connectivity to enable a large number of connected vehicle applications. Vehicular networks allow vehicles to share their emergency and infotainment traffic by following vehicle-to-vehicle (V2V) or by using vehicle-to-infrastructure (V2I) communication. The infrastructure of vehicular networks mainly comprises multiple Road Side Units (RSUs). Fog computing nodes are placed adjacent to these RSUs to provide quick access to vehicles. For infotainment traffic, vehicles intend to download their required content from the content provider. Caching the same contents from the nearby fog computing node significantly reduces delay with improved quality of service. As there are millions of contents with varying sizes, caching all demanded contents on these fog nodes is not possible due to their limited caching capacity. In this work, we propose an improved content caching scheme for fog nodes to satisfy vehicles and content providers for fair content placement. The proposed algorithm is based on a modified Gale–Shapley technique that considers factors such as content popularity, vehicle connectivity, and quality of the communication channel to optimize the content caching process. Simulation results show that the proposed technique caches a higher number of popular contents with lower downloading time.

Traffic congestion is one of the major challenges faced by daily commuters in smart cities. An autonomous transportation system with a 5 G-based Cellular Vehicle-to-Everything (C-V2X) communication system is the solution to meet the traffic challenges faced in smart cities. Vehicular networks provide wireless connectivity to enable a large number of connected vehicle applications. Vehicular networks allow vehicles to share their emergency and infotainment traffic by following vehicle-to-vehicle (V2V) or by using vehicle-to-infrastructure (V2I) communication. The infrastructure of vehicular networks mainly comprises multiple Road Side Units (RSUs). Fog computing nodes are placed adjacent to these RSUs to provide quick access to vehicles. For infotainment traffic, vehicles intend to download their required content from the content provider. Caching the same contents from the nearby fog computing node significantly reduces delay with improved quality of service. As there are millions of contents with varying sizes, caching all demanded contents on these fog nodes is not possible due to their limited caching capacity. In this work, we propose an improved content caching scheme for fog nodes to satisfy vehicles and content providers for fair content placement. The proposed algorithm is based on a modified Gale–Shapley technique that considers factors such as content popularity, vehicle connectivity, and quality of the communication channel to optimize the content caching process. Simulation results show that the proposed technique caches a higher number of popular contents with lower downloading time. [ABSTRACT FROM AUTHOR]

Today, Fog computing plays an important role in minimizing service delivery delays and network relaxation. So many architectures with different purposes are presented in the Fog environment. One of the major drawbacks of most of these architectures is the hierarchical process of servicing requests. Applications are sent to the Fog environment and if Fog is unable to provide the service, it is sent to the Cloud. This process of service is somewhat time-consuming and not suitable for real-time requests and with increasing the number of requests and the number of Fog nodes, choosing the appropriate node to place the application becomes a challenge. In this article to solve these challenges, a framework is provided and some modules are considered that the application specifications are first sent to these modules. These modules contain information about the Fog and Cloud environments, which based on the application specifications in the shortest possible time, determine whether the request is sent to the Cloud or Fog and then the application is sent for service. Therefore, the application that must be served in the Cloud is not entered into the Fog environment. To properly manage the application placement in Fog, some domains are provided for fog nodes, and using a module the most appropriate node for servicing the request is determined. In this framework, service response times and SLA violations are greatly reduced and increasing requests or increasing Fog nodes does not pose a problem in service delivery. The proposed method is implemented in the iFogSim simulation environment with FCFS and PSO algorithms and its results have been evaluated with valid articles. It was observed that the proposed method, on average, reduces the response time by 74.16% compared to the state-of-the-art research works, which is a significant improvement in service response time. [ABSTRACT FROM AUTHOR]

Summary: Cloud storage in the network enables the users to access the data from anywhere irrespective of any devices, which prevents data loss and provides centralized management. The cloud server is a pooled, centrally located server resource that many users can access as needed. More efficient data storage and processing could be achieved through fog computing devices and, the fog node handles the task including the semantic transformation of the data before transmitting it to the back‐end cloud servers, managing the process of data from several edge devices simultaneously. The security of the cloud and fog nodes acts as a key component, and that must be ensured for effective computations. In this research, the security of the fog‐assisted Internet of Things (IoT) network is enhanced using the proposed advanced encryption credibility approach (Ad‐En credibility approach), which ensures the privacy and security of the networks by preventing malicious attempts of accessing the data. The proposed Ad‐En credibility approach is performed based on a modified Diffie‐Hellman algorithm that consists of different phases such as the setup phase, entry and identity phase, fog node entry validation phase, and authentication phase. These phases perform different functions and provide strong authentication mechanisms that secure the data with high confidentiality by detecting malicious attempts within a reduced time. The efficiency of the research is proved by measuring the computational time, memory usage, and detection rate. The proposed Ad‐En credibility approach consumed less time of 7 s and memory usage of 8.17 mb with a high detection rate of 85.34%. [ABSTRACT FROM AUTHOR]

With advent of the Internet of things (IoT), body area networks (BAN) has reached its different new dimension in terms of implementation and smart monitoring applications. Integration of the Internet of things along with the body area network leads to design of pervasive smart and wearable devices. Even though these devices are omnipresence, several design challenges need its solutions for an effective implementation. Energy and performance are the most important challenge which still remains on the darker side of the research. Hence, we proposed energy efficient architecture for IoT-enabled body area networks. The proposed architecture has been designed and developed with the integration of fog nodes along with the extreme learning machine for achieving the low power network and high performance. The proposed BAN architecture has been tested with the different transceivers such as WIFI and ZigBee interfaced with the versions of embedded architectures. Also, the proposed architecture has been compared with existing algorithm such as DARE and M-ATTEMPT in which the energy consumption is reduced to 40% and high performance has been obtained. [ABSTRACT FROM AUTHOR]

Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. However, due to an open environment, UAVs can be hacked to malfunction and report false data. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. In this paper, a fog computing-based smart farming framework is proposed that utilizes UAVs to gather data from IoT sensors deployed in farms and offloads it at fog sites deployed at the network edge. The framework adopts the concept of a charging token, where upon completing a trip, UAVs receive tokens from the fog node. These tokens can later be redeemed to charge the UAVs for their subsequent trips. An intrusion detection system is deployed at the fog nodes that utilize machine learning models to classify UAV behavior as malicious or benign. In the case of malicious classification, the fog node reduces the tokens, resulting in the UAV not being able to charge fully for the duration of the trip. Thus, such UAVs are automatically eliminated from the UAV pool. The results show a 99.7% accuracy in detecting intrusions. Moreover, due to token-based elimination, the system is able to conserve energy. The evaluation of CPU and memory usage benchmarks indicates that the system is capable of efficiently collecting smart-farm data, even in the presence of attacks. [ABSTRACT FROM AUTHOR]

Fog computing enables the data analysis done nearer to the place of data generated, which makes a very short response time. Trust is essential for the effective performance of the fog nodes to overcome uncertainty, vulnerability, and risk. To enhance the trusted connection in fog computing, blockchain technology is adopted as a solution. Inclusion of blockchain in fog environment ensures consistency and security among the fog nodes. Each fog node mines every transaction and stores them as block creating a chain of blocks. In this proposed work, the adaption of blockchain technology is designed as a suitable solution for establishing trusted security between fog nodes and for which a qualitative assessment is done. [ABSTRACT FROM AUTHOR]

The services generated by IoT devices have increased with the increasing demand and capabilities of IoT applications. Proximal fog computing networks efficiently facilitate the latency-sensitive services generated by these devices. A better scheduling technique can effectively minimize the service time of devices. Scheduling using an evolutionary method has been proved to be a better choice for service-time minimization. OBCR (opposition-based chemical reaction) method is developed for task scheduling in fog network by utilizing the features of heuristic upward ranking technique and CRO (chemical reaction optimization) technique with OBL (opposition-based learning). The use of OBL in OBCR results in a more diverse population and aids in escaping the local optima. OBCR uses four operators to provide better exploration and exploitation of the solution space. Furthermore, OBCR also improves the stability of these dynamic fog devices. Extensive simulations have demonstrated the better performance of the proposed OBCR technique compared to other techniques in terms of service-time latency and stability. It also has verified the importance of the technique for fog computing networks. [ABSTRACT FROM AUTHOR]

Forest fire disturbs the balance of the earth's ecological system, increases global warming, flood, drought and decreases the oxygen level. Early identification of forest fires is the only way to mitigate the risk of damages. This motivates authors to design a fog assisted wildfire detection framework using Internet of Things. The proposed model is divided into three layers, such as- data sensing edge layer, fog layer, and cloud layer. The forest area is categorized into different zones. In the data sensing edge layer, the edge nodes of each zone sense the forest fire causing data and transmit it to the corresponding fog devices of the fog layer. Furthermore, an Integrated Rule based classification technique is developed in this paper to detect the status of the zone (VHR/HR/MR/LR) by the fog devices of that zone. This way, edge nodes of LR zones will not transmit data further to the corresponding fog device. As a result, the energy of the edge devices is preserved. Moreover, the cloud layer of the proposed model involves into forest fire monitoring and burnt area prediction using the Keras API model. All the communications are done here applying proposed Modified Greedy Forwarding Algorithm. The fire detection accuracy of proposed FogFire model is 94% which is greater than other existing fire detection techniques. On the other hand, the Qualnet simulator proves that average remaining energy, over all throughput and goodput of the proposed model is also high compared to other IoT communication protocols such as RPL, AODV. [ABSTRACT FROM AUTHOR]

Security is a major challenge in the design and implementation of Internet‐of‐Things (IoT)‐Fog networks and it can be resolved by an efficient authentication mechanism. As the devices have limited resources like battery, processor and memory, any mechanism designed for IoT‐Fog has to be resource aware. In this paper, we proposed a lightweight, secure, dynamic, and efficient scheme for mutual authentication of devices in IoT‐Fog networks. In the proposed scheme, the devices obtain a primary security value from the cloud server and this acquired primary security value is used to authenticate them mutually. We used XOR operation, concatenation operation, one‐way hash function and symmetric key cryptography to accommodate resource constraint nature of devices. The proposed scheme has been analyzed for security formally using BAN logic and automated using Automated Validation of Internet Security Protocols and Applications & ProVerif tools and it is proved that the proposed scheme is secure from the known attacks. The comparative analysis of the proposed scheme with the existing schemes proved that it is better than existing schemes and its experimental analysis showed that it consumes 20%–30% less energy compared to existing schemes. Our proposed scheme is better with reference to security and efficient in respect of computation, communication, and storage overheads. [ABSTRACT FROM AUTHOR]

Due to the massive increase in the Internet of Things (IoT) devices in various applications requiring an IoT–cloud environment, the network latency is high since all the IoT devices have to be authenticated by the cloud servers. Fog nodes can be used as an intercessor between IoT devices and the cloud, thereby reducing the latency of the network since the burden of authenticating the devices can be offloaded from the cloud. This paper proposes a lightweight secure mutual authentication scheme based on physically unclonable function that best addresses the current issues. Since the fog nodes are constrained, a lightweight authentication scheme will be the best solution. The formal security analysis of the scheme LASSI is done using real-or-random model. We used the widely accepted tool Scyther for formal security verification, and the results show that the scheme LASSI is resilient against various attacks. The performance evaluation of the scheme and the comparison with related other schemes show that our scheme is better in terms of communication and computation costs. [ABSTRACT FROM AUTHOR]

Fog computing has the capability to provide computing resources to end-to-end devices like Internet-of-Things (IoT), thereby reducing the burden on the cloud. However, due to the growth of IoT devices and an increase in resource consumption, load balancing in fog computing has turned into a challenging task, since improper load allocation may result in underutilization and overutilization while transferring the tasks from one node to another. In order to solve these challenges, we presented an Energy-efficient load balancing algorithm named Hybrid Priority Assigned Laxity (HPAL) algorithm that allocates the tasks to a suitable Virtual Machine (VM) and completes the task within the minimum time. After the task allocation, the load balancing is handled by calculating the fog optimal time and minimum execution time. Response Time (RT), Processing Time (PT), Delay Time (DT), Execution Time (ET) and Energy Consumption (EC) are the five factors considered in this work to design an energy-efficient load balancing in Fog Nodes (FNs). The proposed algorithm carries two phases, among which in the first phase the task is allocated to each VM according to priority within the fog optimal time and in the second phase, the reallocation of tasks is executed within the minimum execution time considering the energy factor. Therefore, the task migration between the FNs is handled in an energy-efficient manner without affecting the lifetime of the FNs. [ABSTRACT FROM AUTHOR]

Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. However, due to an open environment, UAVs can be hacked to malfunction and report false data. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. In this paper, a fog computing-based smart farming framework is proposed that utilizes UAVs to gather data from IoT sensors deployed in farms and offloads it at fog sites deployed at the network edge. The framework adopts the concept of a charging token, where upon completing a trip, UAVs receive tokens from the fog node. These tokens can later be redeemed to charge the UAVs for their subsequent trips. An intrusion detection system is deployed at the fog nodes that utilize machine learning models to classify UAV behavior as malicious or benign. In the case of malicious classification, the fog node reduces the tokens, resulting in the UAV not being able to charge fully for the duration of the trip. Thus, such UAVs are automatically eliminated from the UAV pool. The results show a 99.7% accuracy in detecting intrusions. Moreover, due to token-based elimination, the system is able to conserve energy. The evaluation of CPU and memory usage benchmarks indicates that the system is capable of efficiently collecting smart-farm data, even in the presence of attacks.

Forest fires can have multiple engenderers including lighting and thunder, unauthorized human exercises, and unrestrained fire projects. In addition to precautions, quick detection, immediate communication, and prompt response are critical to keeping losses to life and property at bay. This work presents Sybil-secured Smart Forest Fire Surveillance based on Blockchain Technology. This work encompasses a detailed three-layer architecture consisting of IoT Layer (sensors planted in the forest), Fog Layer (a decentralized network of fog nodes), and Cloud Layer (Blockchain-based cloud storage capable of computations, decision making, and forecasting). Further, a Blockchain technology-based framework for secured and Sybil-protected data transmission in wildfire scenarios is proposed here. Moreover, an energy-efficient Blockchain consensus algorithm has also been designed and implemented here. Numerous QoS metrics have been considered and used for the evaluation of the proposed system. This work opens the gate to a vista of further research for secure data transmission in wildfire scenarios. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is characterized by the large volumes of data collection. Since IoT devices are themselves resource-constrained, this data is transferred to cloud-based systems for further processing. This data collected over a period of time possesses high utility as it is useful for multiple analytical, predictive and prescriptive tasks. Therefore, it is crucial that IoT devices transfer the collected data to network gateways before exhausting their storage to prevent loss of data; this issue is referred to as the "data offloading problem". This paper proposes a technique for fault tolerant offloading of data by IoT devices such that the data collected by them is transferred to the cloud with a minimal loss. The proposed technique employs opportunistic contacts between IoT and mobile fog nodes to provide a fault tolerant enhancement to the IoT architecture. The effectiveness of the proposed method is verified through simulation experiments to assess the reduction in data loss by use of proposed data offloading scheme. It is demonstrated that the method outperforms a state-of-art method. [ABSTRACT FROM AUTHOR]

Abstract Countless data generated in Smart city may contain private and sensitive information and should be protected from unauthorized users. The data can be encrypted by Attribute-based encryption (CP-ABE), which allows encrypter to specify access policies in the ciphertext. But, traditional CP-ABE schemes are limited because of two shortages: the access policy is public i.e., privacy exposed; the decryption time is linear with the complexity of policy, i.e., huge computational overheads. In this work, we introduce a novel method to protect the privacy of CP-ABE scheme by keyword search (KS) techniques. In detail, we define a new security model called chosen sensitive policy security: two access policies embedded in the ciphertext, one is public and the other is sensitive and hidden. If user's attributes don't satisfy the public policy, he/she cannot get any information (attribute name and its values) of the hidden one. Previous CP-ABE schemes with hidden policy only work on the “AND-gate” access structure or their ciphertext size or decryption time maybe super-polynomial. Our scheme is more expressive and compact. Since, IoT devices spread all over the smart city, so the computational overhead of encryption and decryption can be shifted to third parties. Therefore, our scheme is more applicable to resource-constrained users. We prove our scheme to be selective secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.

Abstract Wireless sensor networks (WSNs) have been recognized as one of the most essential technologies of the twenty-first century. The applications of WSNs are rapidly increasing in almost every sector because they can be deployed in areas where cable and power supply are difficult to use. In the literature, different methods have been proposed to minimize the energy consumption of sensor nodes to prolong WSNs utilization. In this article, we propose an efficient routing protocol for data transmission in WSNs; it is called energy-efficient hierarchical routing protocol for wireless sensor networks based on fog computing. Fog computing is integrated into the proposed scheme due to its capability to optimize the limited power source of WSNs and its ability to scale up to the requirements of the Internet of things applications. In addition, we propose an improved ant colony optimization algorithm that can be used to construct an optimal path for efficient data transmission for sensor nodes. The performance of the proposed scheme is evaluated in comparison with P-SEP, EDCF, and RABACO schemes. The results of the simulations show that the proposed approach can minimize sensor nodes’ energy consumption, data packet losses, and extends the network lifetime. We are aware that in WSNs, the certainty of the sensed data collected by a sensor node can vary due to many reasons such as environmental factors, drained energy, and hardware failures.

Fog computing (FC) models the cloud computing paradigm expedient by bridging the breach between centralized data servers and diverse terrestrially distributed applications. It wields various wireless sensor networks (WSNs) that sprawl in the core of any IoT applications. Consequently, the operation of fog network turns on the efficiency of WSNs operation, while the all-inclusive network energy consumption depends on both FC and WSNs operation. This paper addresses how dissimilar organizations of a fog network can influence its effectiveness and energy savings. Chiefly, it appraises whether deploying multi-sink nodes in close enough vicinity to the fog nodes can give in energy savings and foster coherent data communication between WSNs and fog networks. To assess the multi-sink assignment problem the following four criteria are used: (i) Distance from the fog network nodes; (ii) Nodes degree; (iii) Sink nodes energy; and (iv) Sink nodes processing capabilities. This paper suggests four novel solutions to the multi-sink connectivity for some challenges of fog networks deeming: (i) Window Nondominant Set (WNS); (ii) Evaluation Based Approach (EBA); (iii) Harris Hawks Optimizer (HHO); and (iv) Modified HHO (MHHO). Distinct sets of experiments are conducted to check out algorithmic performance. The performance of all algorithms is measured and then compared to each other in terms of power consumption, runtime, packet loss, and localization error. One of the key supremacies of our approaches is the utilization of fog network for sensor networks data processing, principally with the large-scale networks. Yet, the communication challenges could need further study due to the limited communication range of the sensors.

Fog computing architecture competent to support the mission-oriented network-centric warfare provides the framework for a tactical cloud in this work. The tactical cloud becomes situation-aware of the war from the information relayed by fog nodes (FNs) on the battlefield. This work aims to sustain the network of FNs by maintaining the operational efficiency of the FNs on the battlefield at the tactical edge. The proposed solution monitors and predicts the likely overloading of an FN using the long short-term memory model through a buddy FN at the fog server (FS). This paper also proposes randomised task scheduling (RTS) algorithm to avert the likely overloading of an FN by pre-empting tasks from the FN and scheduling them to another FN. The experimental results demonstrate that RTS with linear complexity has a schedulability measure 8% - 26% higher than that of other base scheduling algorithms. The results show that the LSTM model has low mean absolute error compared to other time-series forecasting models. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The internet of mobile things is subjected to execute on data centers such as cloudlet, cloud servers and also on devices; it solves the problem of multi-objective optimization and tries to discover active scheduling with low energy consumption, execution time and cost. OBJECTIVES: To alleviate the conflicts between the support constraint of 'smart phones and customers' requests of diminishing idleness as well as extending battery life, it spikes a well-known wave of offloading portable application for execution to brought together server farms, for example, haze hubs and cloud workers. METHODS: The test to develop the methodology for mobile phones, with enhanced IoT execution in cloud-edge registering. Then, to assess the feasibility of our proposed process, tests and simulations are carried out. RESULTS: The simulator is used to test the algorithm, and the outcomes show that our calculations can lesser over 18% energy utilization. CONCLUSION: The optimization approaches using PSO and GA based on simulation data, with the standard genetic algorithm providing the highest overall value for mission offloading in fog nodes using multi-objectives. With the assumption of various workflow models as single and multi-objective in data centers as cloud servers, fog nodes, and within computers, we extracted the analytic results of energy usage, delay efficiency, and cost. Then formulated the multi-objective problem with different constraints and solved it using various scheduling algorithms based on the obtained data. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The internet of mobile things is subjected to execute on data centers such as cloudlet, cloud servers and also on devices; it solves the problem of multi-objective optimization and tries to discover active scheduling with low energy consumption, execution time and cost. OBJECTIVES: To alleviate the conflicts between the support constraint of ‘smart phones and customers' requests of diminishing idleness as well as extending battery life, it spikes a well-known wave of offloading portable application for execution to brought together server farms, for example, haze hubs and cloud workers.METHODS: The test to develop the methodology for mobile phones, with enhanced IoT execution in cloud-edge registering. Then, to assess the feasibility of our proposed process, tests and simulations are carried out.RESULTS: The simulator is used to test the algorithm, and the outcomes show that our calculations can lesser over 18% energy utilization.CONCLUSION: The optimization approaches using PSO and GA based on simulation data, with the standard genetic algorithm providing the highest overall value for mission offloading in fog nodes using multi-objectives. With the assumption of various workflow models as single and multi-objective in data centers as cloud servers, fog nodes, and within computers, we extracted the analytic results of energy usage, delay efficiency, and cost. Then formulated the multi-objective problem with different constraints and solved it using various scheduling algorithms based on the obtained data.

Fog Computing is considered as incipient technology in today's fog environment. Fog nodes play an important role between IOT devices and cloud servers during communication. For the implementation and smooth functioning of IOT environment, numbers of IOT devices regularly communicate with Cloud servers. Lots of data need to be sent and receive between IOT devices and cloud servers. During this journey challenges are also striking and increased the problems with real-time interaction, latency, security, quality of services, and delay across the journey of data. Computer system is plugged onto the periphery of the network near IOT devices in order to reduce the above said challenges. These systems are called "fog nodes". The IOT devices can process information with Fog Nodes without moving to Cloud data centers. The main motive behind this paper is to work on well-organized key spreading and mutual validation using MNSOR (Modified Needham Schroeder and Otway Ress) Protocol in fog computing environment to ensure timely interaction excluding clock synchronization, MITM attack protection of key given by registration authority which moves from Cloud Data Center to fog Nodes. [ABSTRACT FROM AUTHOR]

Countless data generated in Smart city may contain private and sensitive information and should be protected from unauthorized users. The data can be encrypted by Attribute-based encryption (CP-ABE), which allows encrypter to specify access policies in the ciphertext. But, traditional CP-ABE schemes are limited because of two shortages: the access policy is public i.e., privacy exposed; the decryption time is linear with the complexity of policy, i.e., huge computational overheads. In this work, we introduce a novel method to protect the privacy of CP-ABE scheme by keyword search (KS) techniques. In detail, we define a new security model called chosen sensitive policy security: two access policies embedded in the ciphertext, one is public and the other is sensitive and hidden. If user's attributes don't satisfy the public policy, he/she cannot get any information (attribute name and its values) of the hidden one. Previous CP-ABE schemes with hidden policy only work on the "AND-gate" access structure or their ciphertext size or decryption time maybe super-polynomial. Our scheme is more expressive and compact. Since, IoT devices spread all over the smart city, so the computational overhead of encryption and decryption can be shifted to third parties. Therefore, our scheme is more applicable to resource-constrained users. We prove our scheme to be selective secure under the decisional bilinear Diffie-Hellman (DBDH) assumption. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have been recognized as one of the most essential technologies of the twenty-first century. The applications of WSNs are rapidly increasing in almost every sector because they can be deployed in areas where cable and power supply are difficult to use. In the literature, different methods have been proposed to minimize the energy consumption of sensor nodes to prolong WSNs utilization. In this article, we propose an efficient routing protocol for data transmission in WSNs; it is called energy-efficient hierarchical routing protocol for wireless sensor networks based on fog computing. Fog computing is integrated into the proposed scheme due to its capability to optimize the limited power source of WSNs and its ability to scale up to the requirements of the Internet of things applications. In addition, we propose an improved ant colony optimization algorithm that can be used to construct an optimal path for efficient data transmission for sensor nodes. The performance of the proposed scheme is evaluated in comparison with P-SEP, EDCF, and RABACO schemes. The results of the simulations show that the proposed approach can minimize sensor nodes' energy consumption, data packet losses, and extends the network lifetime. We are aware that in WSNs, the certainty of the sensed data collected by a sensor node can vary due to many reasons such as environmental factors, drained energy, and hardware failures. [ABSTRACT FROM AUTHOR]

Recently Internet of Everything has emerged as integration of various IoT machines to which cloud computing provide storage to data and processing power to the geographically distributed IoT devices. In order to boost the efficiency of the system and Quality of Service(QoS), fog layer is added in the existing cloud infrastructure. Due to continuous increase in the time sensitive applications, reduction in latency is a crucial issue in the fog computing paradigm. Therefore, the main objective of this work is to reduce the latency in fog computing. To achieve this objective, popularity based caching is performed in this work by majorly focusing on the interest of the users. In this context, first clustering of the IoT devices is performed on the basis of their interests and distance between them using spectral clustering technique and then each cluster is mapped with the fog node such that caching of the popular files is done effectively. To further reduce the latency, in case of cache miss, the Device to Device (D2D) communication is used. Finally, association rules are also used to predict the future demands of the IoT devices. Performance analysis of the proposed scheme shows that the proposed method outperforms the other existing caching methods. [ABSTRACT FROM AUTHOR]

The industrial applications in the cloud do not meet the requirements of low latency and reliability since variables must be continuously monitored. For this reason, industrial internet of things (IIoT) is a challenge for the current infrastructure because it generates a large amount of data making cloud computing reach the edge and become fog computing (FC). FC can be considered as a new component of Industry 4.0, which aims to solve the problem of big data, reduce energy consumption in industrial sensor networks, improve the security, processing and storage real-time data. It is a promising growing paradigm that offers new opportunities and challenges, beside the ones inherited from cloud computing, which requires a new heterogeneous architecture to improve the network capacity for delivering edge services, that is, providing computing resources closer to the end user. The purpose of this research is to show a systematic review of the most recent studies about the architecture, security, latency, and energy consumption that FC presents at industrial level and thus provide an overview of the current characteristics and challenges of this new technology.

Recent years there has been increasing concern about the rider demand responsive systems and the vehicular ad hoc networks. On one hand, centralised taxi platforms such as Uber and Didi Taxi are popular and changing our daily life; on the other hand, vehicles are equipped with more and more sensors and are capable to calculate, store, and communicate with other vehicles or road side units, forming vehicleto-vehicle or vehicle-to-infrastructure communications. However, little effort has been devoted to integrating these two fields. In this paper, we propose a distributed public vehicle (PV) system that integrates the rider demand responsive system and ad hoc vehicular technologies, where the concept of fog computing and vehicular sensing are adopted for the system design. The challenges lie in that the PV scheduling problem itself is NP-hard, and careful design of scheduling and cooperation schemes among nodes are needed as they are ubiquitously connected at the edge of networks. The proposed PV system adopts a heuristic request insertion algorithm and a cooperative strategy among vehicle nodes, fog nodes, and the cloud to dispatch requests and to schedule routes for PVs. Experimental studies on real-world data sets demonstrate that the proposed scheme achieves higher service ratio of requests and better efficiency than other transit methods. Furthermore, the distributed vehicular sensing is demonstrated to be capable of collecting feasible metadata for scheduling applications. To the best of our knowledge, this paper is the first report on the integration of fog nodes and vehicular sensing for the rider request responsive scheduling systems.

Fog computing uses fog nodes (FNs) near end devices to serve user applications in a highly virtualized environment. FNs are vulnerable to overloading because of the large number of user applications requesting service and their heterogeneous resource requirements. The proposed paper concentrates on maintaining FN in optimal working condition guaranteeing its operational efficiency and quality of service (QoS). This paper presents two methods for workload classification and overload prediction in FN, viz., a threshold-based technique adopting the criteria inferred from experimental results and a method based on Hidden Markov Model (HMM). The paper recommends virtual machine (VM) migration as a solution for avoiding the predicted overloading of FN through a hybrid multiple attribute decision making (MADM) method, for selecting the VM for migration, and an algorithm for finding the destination FN willing to host the migrating VM. Experimental results validate the proposed solution. [ABSTRACT FROM AUTHOR]

Given the recent proliferation in the number of smart devices connected to the Internet, the era of Internet of Things (IoT) is challenged with massive amounts of data generation. Fog Computing is gaining popularity and is being increasingly deployed in various latency-sensitive application domains including industrial IoT. However, efficient discovery of services is one of the prevailing issues in the fog nodes of industrial IoT, which restrain their efficiencies in availing appropriate services to the clients. To address this issue, this paper proposes a novel efficient multilevel index model based on equivalence relation, named the distributed multilevel (DM)-index model, for service maintenance and retrieval in the fog layer of industrial IoT to eliminate redundancy, narrow the search space, reduce both the number of traversed services and retrieval time, ultimately to improve the service discovery efficiency. The efficiency of the proposed index model has been verified theoretically and evaluated experimentally, which demonstrates that the proposed model is effective in achieving much better service discovery and retrieval performance than the sequential and inverted index models. [ABSTRACT FROM AUTHOR]

Cloud computing with its services models (SaaS, PaaS, and IaaS) and its characteristics such as (on demand services, rapid elastics, and scalability) still has a number of problems that prevent some applications from the advantages of this computing paradigm. The long distance between the end-users and the cloud server might be the biggest issue for some real time applications, such as online gaming and content delivery, in term of latency. Fog computing is an emerging paradigm used to solve some issues in cloud computing. It empowers deploying the resources and the services at the edge of the network that is close to end users. The use of streaming services for sensor networking applications is a very important model in machine-to- machine paradigm of communication. In this paper we propose a system for content delivery services for mobile users using fog computing. The fog nodes are distributed at the edge of the network, in the middle between the cloud and mobile users. These nodes receive requests from the users and respond to each request either immediately, in case the service is already downloaded in the fog server, or by bringing the servers either from the cloud server or other fog nodes. The fog nodes are represented as server using COAP protocol. As expected, the results show fog computing provides low response time against the cloud computing. The results also, investigate the power consumption of mobile users for the four dierent modes, Low power mode, Transmission mode (Tx), Receiving mode (Rx), and CPU when they connect directly to fog servers. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EV) has gained immense popularity due to the increasing awareness amongst people regarding low carbon emission. Smart vehicles have become a central part of the Internet of vehicles (IoV) infrastructure. Whenever several vehicles distribute its tasks, then the classical centralized model meet various issues, such as security and delay in communication. This paper devises a technique for energy harvesting in the Fog-IoV network. The Fog-IoV network simulation is done for enhanced processing. The three layers, such as fog layer, cloud layer, and IoV layer are adapted for electricity trading. The power prediction is performed with a deep reinforcement learning technique, namely Deep Q network (DQN). The optimal electricity trading is done with the proposed Adam Remora Optimization Algorithm (AROA). The AROA is obtained by the amalgamation of Remora Optimization Algorithm (ROA) and Adam optimization algorithm. The EVs represents buyer of electricity that demands electricity in such a way that Road side unit (RSU) perform bidding. The fitness function is newly modelled using predicted power, price, and distance. The experimentation of the technique is done in terms of fitness, power, and pricing. The proposed AROA-based DQN offered enhanced performance with the highest power of 11.920 and the smallest pricing of 16.949%. • This paper devises a technique for energy harvesting in the Fog-IoV network. • The power prediction is performed with deep reinforcement learning technique, namely Deep Q network (DQN). • The optimal electricity trading is done with proposed Adam Remora Optimization Algorithm (AROA). • The fitness function is newly modelled using predicted power, price and distance. [ABSTRACT FROM AUTHOR]

The field of Internet of Things (IoT) is evolving at an exponential rate where megabytes of data is being processed each passing minute. Fog computing is an emerging field, regarded as the new way forward to prevent the suspected data outburst of the exhausted IoT devices. Fogging is believed to reduce latency, and enhance efficiency, ease of deployment and flexibility, however, various security and privacy concerns hinder its deployment by major platforms. Among all the security concerns, lack of a proper trust management system is of primary importance. This paper explores the Service-oriented Internet of Things (SIoT) domain to study its similarity with Fogging and creates a trust management scheme (TMS). The paper further explores the necessary trust requirements for Fogging and incorporates them into a two-way trust management scheme based on Bayes model, the model allows both the service requestor and the service provider to validate each other before connecting. The model calculates the value of trust using these metrics and combines them in a unique way with Bayes trust to have an accurate trust value. The proposed scheme is simulated in Netlogo, an agent-based network simulator. The subject scheme is capable of effectively preventing a legitimate node from connecting with a malicious node. The results illustrates high accuracy and faster convergence and also shows resilience against trust-based network attacks. The system is compared against SIoT trust management models due to lack of similar trust management models in Fog Computing. [ABSTRACT FROM AUTHOR]

The research on the Vehicular ad-hoc network (VANET) has been accelerated by the 5 G technology. The software-defined network and fog nodes near the vehicles have improved the throughput and latency in the processing of requests. However, the fog nodes are limited with computational resources like memories, RAM, etc. and need to be optimally managed. The estimation of vehicles' future locations can help in the optimal offloading of vehicles' processing requests. This paper has introduced the Kalman filter prediction scheme to estimate the vehicle's next location so that the future availability of fog resources can help in the offloading decision. The deep Q network-based reinforcement learning is used to select the resources-rich fog node in VANET. The Long Term Short Memory-based Deep Q-Network optimally offloads the tasks of the fog nodes as per their available resources thus giving much better performance. The proposed Deep Q-Network algorithm is an efficient solution to offload the request optimally which improves the overall performance of the network. It is found that the average reward by proposed Deep Q-Network is 56.889% more than SARSA learning and is 44.727% more than Q learning. [ABSTRACT FROM AUTHOR]