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Summary: With the development of 5G technology, the Internet of Things (IoT) system is becoming more and more widely used in various fields. However, the reliability issue still hinders the wide applications. For instance, the transmission reliability of the IoT system will be significantly affected by the status of end devices, wireless channel quality, and the environment. Moreover, according to the Automatic Repeat‐reQuest (ARQ) retransmission mechanism in the 802.15.4 protocol, if the IoT end devices pursue a conservative power model causing low signal transmission power, it is likely that the signal power will lose too much during the transmission process. This will result in the increase of the network packet loss rate and the power consumption during the data retransmission. To solve this problem, this paper proposes a reasonable transmission power allocation algorithm to ensure the transmission reliability, considering the ARQ retransmission mechanism. The algorithm intends to find the optimal transmission power of each IoT end device, so as to minimize the total energy consumption. The simulation results demonstrate that the power allocation algorithm improves the reliability of the IoT system, compared with other algorithms. [ABSTRACT FROM AUTHOR]

Summary: Fire is a serious threat to human life and property, and effective prediction of fire can reduce the harm of fire to human. In the research of fire alarm automation, the fire alarm system based on wireless sensor network is a common method. When using wireless sensor to monitor the fire alarm information, it is necessary to ensure the safety coverage of the network in the monitoring area. Therefore, it is very necessary to study the network coverage in the fire alarm monitoring, which can ensure the effective work of the monitoring network, and good coverage control can reduce the energy consumption of the network and extend the service life of the network. Based on this, this paper designs a network coverage control algorithm based on multiobjective genetic algorithm optimization considering the coverage and energy consumption of wireless network. In this algorithm, the probability perception model is used to design the network coverage control model for the monitoring area with different coverage requirements, taking the maximum area coverage and the minimum network energy consumption rate as the optimization goal. Then the genetic algorithm is used to solve the model. In addition, because the multiobjective genetic algorithm is easy to fall into the local optimal solution when solving the optimization problem, this paper introduces the cross and variation coefficients of self‐adaptive adjustment to improve the genetic algorithm. Through the simulation analysis, it is shown that when solving the multiobjective optimization problem, the introduction of self‐adaptive adjustment of cross and variation coefficients can well overcome the problem that genetic algorithm falls into the local optimal solution, whereas the network coverage control algorithm designed in this paper based on the multiobjective genetic algorithm optimization can achieve greater coverage and lower energy consumption. Compared with the network coverage control algorithm before optimization, the optimized algorithm improves greatly in coverage, network residual energy, and number of nodes, which can well extend the service life of wireless sensor network, and is conducive to the good application of wireless sensor in fire prediction. [ABSTRACT FROM AUTHOR]

Summary: Terrorist attacks have contributed significantly to using wireless technologies to identify concrete destruction survivors. A dynamic ad hoc mobile network (MANET) consists of wireless linked nodes, which route hop‐by‐hop without the support of a fixed infrastructure acquires information from trapped survivors. The energy efficiency that extends the lifespan of the network is an essential prerequisite of MANET. Researchers have suggested many strategies to accomplish this purpose and a cluster of these techniques in MANETs are used to provide an energy‐efficient approach. In this paper we are proposing a red deer multi‐objective constraint applied for an energy efficient QoS routing (RD‐MOCER) algorithm to the number of clusters in an ad hoc network and the energy dispensing in nodes to provide an energy effective solution and to minimise network traffic. Intracluster and intercluster traffic is handled by the cluster heads in the proposed approach. The algorithm suggested takes account of mobile nodes' node degrees, transmitting capacity and battery power usage. This approach gives a variety of options at a time, the key benefit of which is that the ideal Pareto front results in these solutions. We correlate the findings with two other well‐known methods of clustering; MOPSO and MOEAQ‐based clustering with different results. We conduct detailed simulations to demonstrate that the solution proposed is an effective and stronger solution to clustering in ad hoc cell networks than the other two techniques. [ABSTRACT FROM AUTHOR]

Summary: Providing the appropriate coverage is quite essential for the effective functioning of many applications in Wireless Sensor Networks. Therefore, the efficiency of node deployment algorithms to supply the requested coverage is of high significance. In this paper, inspired by the equilibrium of molecules, a novel node deployment algorithm, called Smart Self‐organising Node Deployment (SSND), is proposed to provide maximum coverage. Despite other proposed algorithms, which provide coverage based on the collective movement of nodes with massive energy consumption, SSND moves one sensor in every neighbourhood at each step to reduce the sensor nodes' movement and hence the energy consumption. The chosen sensor nodes at each time step are distributedly determined by an eligibility function to reduce the non‐essential movements while improving the accuracy of the reported locations of neighbours. Our extensive simulation study shows that SSND can achieve up to 30% coverage improvement compared to those of other algorithms in most scenarios and provides an adequate trade‐off between coverage and energy consumption. [ABSTRACT FROM AUTHOR]

Summary: Heterogeneous networks (HetNets) have been a trending topic of interest for researchers in 5G technology. A HetNet structure comprises a macro cell network assisted by small cell networks such as pico cells and femto cells. This additional hardware ensures distribution of the user equipment (UE) load of the main base station (MBS) at the cost of a surge in the overall system power consumption. Optimized power consumption coupled with enhanced cell throughput by dynamic small cell ON/OFF strategy improves the energy efficiency (EE) in dense HetNets. This paper proposes two algorithms to switch the small cell ON/OFF based on cell throughput contribution rate (CTCR). CTCR is the ratio of actual cell throughput to the maximum cell throughput with full utilization of the allotted bandwidth. In the first method, the threshold to decide small cell ON/OFF has been carefully defined considering two important factors—the MBS‐SBS distance and the ratio of small cell density to UE density such that less loaded small cells that are closer to the MBS are the candidates chosen for sleep mode. In the second method, a correction factor in the computation of CTCR is introduced. It is a logarithmic function of the relative distance of the small base station (SBS) to the radius of macro cell coverage. This steps up the threshold for SBS that are close to MBS. They are more suitable sleep state candidates as their UEs can be served directly by the MBS and enable large user throughput. Simulation results show that the EE of the proposed amended CTCR method is 8% more than proposed CTCR method and 30.66% better relative to conventional load‐based sleep control method. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we mainly focus on the influence of network partitioning on the network and develop a new Multi‐chargers region Partition charging Scheduling Algorithm (MPSA) based on network information. By studying the influence of the different number of partitions and partition mode on the whole sensor networks, and simultaneously comparing different MCs scheduling algorithms from multiple perspectives, such as the effective energy usage efficiency and the survival rate of sensor nodes in the whole network, the superiority of Multi‐chargers region Partition charging Scheduling Algorithm (MPSA) scheduling strategy and the rationality and necessity of layered mechanism are proven. Simulations are conducted to verify the advantages of our scheme, revealing that our scheme outperforms in terms of dead node number, energy usage efficiency, and so on. [ABSTRACT FROM AUTHOR]

Summary: The progressive strides in the exploration of sensing technology and the potential use of electrical devices have rendered a promising technology, termed as Wireless Body Area Networks (WBANs). This technology has rendered competency to reform healthcare and making available ubiquitous health care support to remotely located patients. However, the sensor nodes which are embedded on or underneath the body of the patients to measure multifaceted physiological parameters are suffered from the limited battery constraints. In this paper, to address this issue, we propose Intelligent‐Routing Algorithm for WBANs (I‐RAW) to elongate life period of these sensor nodes. The two sinks are located on the front and back side of the patient's body that collects data from the sensor nodes which form clusters and forward the data through the cluster head (CH). We apply Tunicate Swarm Algorithm (TSA) for selection of CH which considers the essential parameters namely, residual energy, network's average energy, distance of node from the sink, path loss model, and energy consumption rate. The use of two sinks in WBAN mitigate hot‐spot problem in the network by avoiding the multi‐hop communication. The simulation results show that I‐RAW improves stability period and network operational period by 37.7% and 42.7%, respectively, as compared to Dual Sink approach using Clustering in Body area network (DSCB) protocol, respectively. Further, I‐RAW also shows supreme performance for various performance metrics as compared to other state‐of‐the‐art protocols. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) as an emerging technology is going to play a critical rule in the physical‐cyber ecosystem. In this environment, sensing and actuating are considered as services for building intelligent applications. From performance point of view, a main question is how to locate these services on devices can realize a remarkable cost‐efficiency and energy saving. In this paper, the goal is to specify which accompanying service with another specific service can be set on a device to optimize the energy consumption, load amount on devices, and the service response time. For this purpose, we formulate this goal as a multiobjective decision‐making problem, and then we solve it by using the PROMETHEE method. In this solution, the utility of each device is determined and estimated based on entropy technique that used for computing weights for different criteria. Numerical simulation results show that the proposed co‐locating algorithm, called p‐co‐locating, outperforms none‐co‐locating approach in terms of energy consumption, load amount, and the service response time. [ABSTRACT FROM AUTHOR]

Summary: Shared transportation is a new way of traveling generated by the rapid development of the "Internet +" and the sharing economy. Due to the limited energy carried by shared e‐bikes, the energy problem becomes the primary obstacle restricting its development. At present, one way to solve the energy problem is to use mobile charging vehicles (MCVs). However, how to schedule MCVs to achieve fast response, reduce energy consumption, and improve charging efficiency is crucial. In this paper, we propose a Charging Scheduling scheme based on Dynamic Characteristic of shared e‐bike in Obstacle Space (CSDC‐OS). Firstly, we design two MCV departure mechanisms to solve the problems of low utilization of MCVs and long waiting time for shared e‐bikes. Then, we design the schedulability conditions based on the A‐star algorithm and the dynamic change of e‐bikes to ensure that MCVs can return to the service station in time. Finally, we propose a charging sequence algorithm based on linear skyline query and a selective insertion algorithm to improve the charging efficiency of MCVs. Simulation results show that the CSDC‐OS can improve the response time, mobile consumption, and charging efficiency by more than 8% on average. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensors are considered the key elements in enabling efficient Internet of Things (IoT) networking and services. One of the key objectives in designing a WSN is increasing network lifetime by reducing the overall network energy consumption. To achieve this, several techniques have been proposed to extend the network lifetime, such as clustering. Clustering divides the network into several clusters, each with its own member nodes and a cluster‐head (CH) node. In this paper, a novel clustering mechanism is developed with the objective of extending network lifetime through load balancing in wireless sensor networks (WSNs). This is achieved by electing a new node to a provides specific tasks in the WSN to decrease energy consumption and enhance network lifetime. This node is called a relay node that is responsible of delivering the received data packets from the CHs, and then forwards them to the central node (sink). Unlike previous clustering schemes, the proposed algorithm consists of three main processes: CH selection, relay node selection, and medium access control processes. These processes depend on energy level and user's distributions. The performance of the proposed algorithm is investigated through simulation experiments in terms of network lifetime under various network parameters. The results reveal that the proposed mechanism significantly enhances network lifetime compared to previously proposed algorithms. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSN) encompasses millions of nodes that work jointly, senses environmental information, and transmits to the Base Station (BS). "Energy hole" or "hot‐spot" is a problem in which sensor nodes near to the BS deceasetoo early which creates the partitioning of the network. This is due to the reason of the difference in the consumption of energy by the nodes in WSN which reduces the lifespan of the sensor network. The distribution of the cluster heads and the load balancing are the major pitfalls. In this paper to deal with the statedpitfalls, a hybrid of fuzzy logic technique and harmony search algorithm is proposed to lengthen the lifetime of the network. Unequal clustering is a technique proposed by the researchers to handle the hot‐spot issue, hence it is also included. The proposed algorithm forms unequal clusters. To prove the efficiency of the proposed algorithm, some well‐known unequal clustering algorithms and harmony search based algorithmare used to compare the proposed algorithm under different network settings. In all the settings, better performance is achieved by the proposed protocol. It prolongs the lifespan of the network as compared to the different algorithms proposed so far. [ABSTRACT FROM AUTHOR]

Summary: The widely deployed wireless sensor networks, incorporated with the capabilities of sensing, processing, and communicating, require more effective routing to maintain the balance between the performance parameters and to be prevented from the security attacks. The cryptographic methods used were effective but consumed more energy on the authentication, the encryption, and the decryption. So the routing addressing the security problems, with the reduced energy consumption, latency, and PDR losses, maximizing the network lifetime is proposed in the paper. This multiobjective routing problem of the sensor networks is handled using the ant colony routing techniques, taking into consideration the remaining energy of the nodes, the distance between the nodes, and the trust credentials of the nodes, to reduce the energy consumption and enhance the lifetime of the sensor networks. The further performance evaluation using the network simulator‐2 reveals the efficiency and the quality of the service of proposed routing technique over existing techniques on the grounds of energy utilization, PDR, lifetime of sensor networks, and the security enhancement rate. [ABSTRACT FROM AUTHOR]

Summary: Energy efficiency and data reliability are the crucial issues to be explored in wireless sensor networks (WSNs). A large amount of power consumption is depleted by each sensor node to transmit its sensed data which typically induces high temporal and spatial dependencies. Therefore, efficient compression approaches are designed to exploit the data correlation in both space and time so as to discard information redundancies with accurate recovery. In this paper, we propose a hybrid and adaptive spatiotemporal compression approach named Distributed Temporal Source Coding, designed to extend the lifespan of cluster based WSNs. The proposed scheme takes advantage of the performed Distributed Source Coding as an effective spatial compression algorithm and adaptively exploits the Lightweight Temporal Compression as a promising solution to reduce the temporally correlated information. The obtained results have revealed that the proposed algorithm outperforms other compression methods, promotes the network battery life, and ensures compelling data accuracy. The proposed scheme has succeeded to reduce the gap between the energy efficiency and data reliability in WSNs. [ABSTRACT FROM AUTHOR]

Summary: Due to the exponential rise in the number of subscribers in existing wireless networks, energy‐efficient distribution and utilization of network resources have become the preliminary objective of researchers nowadays. Nonorthogonal multiple access (NOMA) has attained widespread significance in this regard and has become a strong candidate for enhancing energy efficiency (EE) performance of existing networks. Multicarrier NOMA (MC‐NOMA) technique is an extension of NOMA that breaks the available resource block into various subchannels and allocates them efficiently to NOMA users (NUs). MC‐NOMA networks have the ability to further enhance system performance by efficiently exploiting channel diversity for addressing the problems of spectral inefficiency and power limitations. Therefore, in this paper, we have considered MC‐NOMA technology for an uplink scenario to investigate its performance for enhancing system EE by performing energy‐efficient power and subchannel allocation. To this end, we have formulated a joint user clustering, subchannel allocation, and power allocation problem for EE maximization (JSPEE) of uplink MC‐NOMA scenario. Due to the nonconvex, combinatorial nature of the problem, we propose a two‐step solution: that is, for each subchannel, first, subchannel allocation and user clustering are attained through low‐complexity suboptimal algorithm, which is followed by energy‐efficient power allocation of NUs. For subchannel allocation and user clustering, we exploit the channel diversity in the form of difference in channel gain values of various users. For power allocation, the nonconvex nature of the formulated problem is tackled by employing Dinkelbach and successive convex approximation (SCA) techniques to attain an energy‐efficient solution. Our simulation results portray that the JSPEE algorithm clearly outperforms the current NOMA as well as OMA works and enhances the system EE by efficient user clustering and exploiting channel diversity. [ABSTRACT FROM AUTHOR]

Summary: This paper introduces a new way to allocate the modulation format and improves the energy efficiency in elastic optical networks. For every connection request, both the quality of transmission and quality of service requirements are investigated. The allocation of modulation formats is based on both the physical distance and the required number of frequency slots. To improve the energy efficiency, Yen's algorithm is executed for every connection requested, and the lightpath with the lowest power consumption is selected. Numerical results were compared with adaptive modulation algorithms presented in the existing literature. The energy efficiency was improved by up to 27%, the used bandwidth available was reduced by up to 60%, and the blocking probability was decreased by 30% using our proposed adaptive algorithm. [ABSTRACT FROM AUTHOR]

Summary: Wireless video sensor network (WVSN) has attracted much research interest due to its multi‐faceted potential applications in different fields. An energy‐efficient protocol is fundamental for WVSN since the network has to deal with traffic‐intensive multimedia data, such as images and videos. A few issues come in front for any such setting like energy inefficiency, throughput unfairness, and low network capacity. In many cases, we need to strike a balance between required parameters or aspects, but there is no alternative to ensuring appropriate energy efficiency in the process. Keeping this in mind, this paper proposes a holistic solution named energy‐efficient bio‐inspired Mimosa pudica (EEBIMP) algorithm. EEBIMP for WVSN can reduce the time delay and avoid unnecessary energy consumption due to clustering the network. Our proposed algorithm has been compared with one of the most recent notable solutions, like GREENNESS (Green wiReless vidEo sENsor NEtworks uSing out‐of‐band Signalling), PSO (Particle Swarm Optimization), and CUCKOO search algorithm. The result shows that EEBIMP achieved 95% of energy efficiency and throughput when compared with other algorithms of GREENNESS, PSO, and CUCKOO. [ABSTRACT FROM AUTHOR]

Summary: Energy conservation in Wireless Sensor Networks (WSNs) is essential for improving the stability of sensor nodes and network lifetime. An energy‐proficient clustering‐based routing protocol is necessary for efficient cluster formation and Cluster Head (CH) selection to sustain Quality of Service (QoS). In this paper, an Improved Bkd‐Tree‐Inspired Energy‐eFficient Clustering‐based Routing Protocol (IBkd‐Tree‐IEFCRP) is propounded as a potential strategy for guaranteeing significant cluster formation and management. It is proposed as an effective strategy in CH node allocation that handles the impacts of QoS during the process of data dissemination. This IBkd‐Tree‐IEFCRP is proposed to deal with energy saving with respect to data traffic by including a strategy of cluster formation which incorporates the benefits of partition data structure that incorporates an improved Bkd‐tree algorithm. It also concentrates on the process of CH selection by integrating a reactive approach that enhances throughput with minimized energy, delay and jitter. The simulation experiments of the proposed IBkd‐Tree‐IEFCRP confirm a better performance in improving the average throughput by 13.21%, reducing the energy consumptions by 12.96%, minimizing the delay by 11.98% and reducing the jitter by 14.24% when compared to the baseline k‐dimensional tree algorithm (kd‐tree algorithm), Weighting and Parameter Optimization‐based Energy‐Efficient Clustering Routing Protocol (WPO‐EECRP), Improved Clustering by Fast Search and Finding of Density Peaks (ICFSFDP), and Energy‐based Cluster Centered Routing Protocol (ECCRP) taken for comparison. The proposed IBkd‐Tree‐IEFCRP is also identified to reduce delay by 8.42% and jitter by 7.18% and increase throughput by 6.84% when compared to the benchmarked protocols for different number of rounds. [ABSTRACT FROM AUTHOR]

Summary: In order to support the demand of the users in the new generation wireless systems (such as the smart interaction between the mobile devices and the mobile human), the resources of the intelligent transportation system should be utilized efficiently. Thus, the network structure adaptation due to the rapid changes in such communication process should be investigated. This is in addition to save the direct impact of the network resources such as high speed data transmission and data processing. Based on the standards for both of vehicular ad hoc network (VANET) and IEEE 802.11p, two main propositions have been examined to attain the improvement in such intelligent systems performance. In this paper, we improved the QoS and proposed a clustering algorithm named MP‐RC‐MBOFDM. It groups the mobile nodes based on their speeds with some modified head assignments processes. This will be combined with a parallel‐processing technique and a wavelet processing stage to optimize the transmitted power phenomenon. The simulation results of five main efficiency factors that have been involved, namely, complementary cumulative distributions, bit rates, energy efficiency, the cluster head life time, and the ordinary nodes reattaching‐head average times, show the feasibility of the new proposition in the V2V data transmission. [ABSTRACT FROM AUTHOR]

Summary: There is a good opportunity for enlightening the services of the mobile devices by introducing computational offloading using cloud technology. Offloading is a process for managing the complexity of the mobile environment by migrating computational load to the cloud. The mobile devices oblige the quick response for the offloading requests; it is dependent on network connectivity. The cloud services take long set‐up time irrespective of network connectivity. In this paper, new system architecture for the dynamic task offloading in the mobile cloud environment is proposed. The architecture includes the offloading algorithm that concentrates on energy consumption of the tasks both in the local and remote environment. The proposed algorithm formulates a collective task execution model for minimizing the energy consumption. The architecture concentrates on the network model by considering the task completion time in three different network scenarios. The experimental results show the efficiency of the suggested architecture in reducing the energy consumption and completion time of the tasks. [ABSTRACT FROM AUTHOR]

Summary: It is a visible fact that the growth of mobile devices is enormous. More computations are required to be carried out for various applications in these mobile devices. But the drawback of the mobile devices is less computation power and low available energy. The mobile cloud computing helps in resolving these issues by integrating the mobile devices with cloud technology. Again, the issue is increased in the latency as the task and data to be offloaded to the cloud environment uses WAN. Hence, to decrease the latency, this paper proposes cloudlet‐based dynamic task offloading (CDTO) algorithm where the task can be executed in device environment, cloudlet environment, cloud server environment, and integrated environment. The proposed algorithm, CDTO, is tested in terms of energy consumption and completion time. [ABSTRACT FROM AUTHOR]

Summary: This paper deals with the lifetime problem in the Internet of Things. We first propose an efficient cluster‐based scheme named "Cuckoo‐search Clustering with Two‐hop Routing Tree (CC‐TRT)" to develop a two‐hop load‐balanced data aggregation routing tree in the network. CC‐TRT uses a modified energy‐aware cuckoo‐search algorithm to fairly select the best cluster head (CH) for each cluster. The applied cuckoo‐search algorithm makes the CH role to rotate between different sensors round by round. Subsequently, we extend the CC‐TRT scheme to present two methods for constructing multi‐hop data aggregation routing trees, named "Cuckoo‐search Clustering with Multi‐Hop Routing Tree (CC‐MRT)" and "Cuckoo‐search Clustering with Weighted Multi‐hop Routing Tree (CC‐WMRT)." Both CC‐MRT and CC‐WMRT rely on a two‐level structure; they not only use an energy‐aware cuckoo‐search algorithm to fairly select the best CHs but also adopt a load‐balanced high‐level routing tree to route the aggregated data of CHs to the sink node. However, CC‐WMRT slightly has a better performance thanks to its low‐level routing strategy. As an advantage, the proposed schemes balance the energy consumption among different sensors. Numerical results show the efficiency of the CC‐TRT, CC‐MRT, and CC‐WMRT algorithms in terms of the number of transmissions, remaining energy, energy consumption variance, and network lifetime. [ABSTRACT FROM AUTHOR]

Energy efficient data collection in a delay-bound application is a challenging issue for mobile sink-based wireless sensor networks. Many researchers have proposed the concept of rendezvous points (RPs) to design the path for the mobile sink. Rendezvous points are the locations in the network where the mobile sink halts and collects data from the nearby sensor nodes. However, the selection of RPs for the design of path has a significant impact on timely data collection from the network. In this paper, we propose an efficient algorithm for selection of the RPs for efficient design of mobile sink trajectory in delay-bound applications of wireless sensor networks. The algorithm is based on a virtual path and minimum spanning tree and shown to maximize network lifetime. We perform extensive simulations on the proposed algorithm and compare results with the existing algorithms to demonstrate the efficiency of the proposed algorithm of various performance metrics. [ABSTRACT FROM AUTHOR]

SUMMARY Cloud computing has emerged as a promising technique to provide storage and computing component on-demand services over a network. In this paper, we present an energy-saving algorithm using the Kalman filter for cloud resource management to predict the workload and to further achieve high resource availability with low service level agreement. Using the proposed algorithm, one can estimate the potential future workload trend then predict the computing component workload utilizations and further retrench energy consumption and achieve load balancing in a cloud system. Experimental results show that the proposed algorithm achieves more than 92.22% accuracy in the computing component workload prediction, improves 55.11% energy in energy consumption, and has 3.71% in power prediction error rate, respectively. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]