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Maintaining prolonged service lifetime and adequate quality of sensing coverage are the key challenges in constructing Wireless Sensor Network (WSN) based applications. As such networks usually operate in inhospitable and hostile environment, failures are ineludible and providing resilience is a necessity. However, it is challenging to satisfy the conflicting problems of enhancing energy efficiency and fault tolerance simultaneously. Fault-tolerance is a significant requirement while designing WSN. It is crucial to detect the failures in advance and take necessary measures to maintain durable and efficient functioning of the network. Generally, in the existing face structured WSNs, node faults and failures can induce the formation of coverage holes, disrupt the face structure and consequently curtail the application performance. The coverage quality will affect the monitoring effectiveness of tracking applications, e.g., a moving target tracking. Moreover, node failures can cause the network to be partitioned, further reducing the accuracy in tracking. In this paper, we propose a robust fault-tolerance scheme with coverage preservation using a face structured WSN topology ( F CAFT ). The key objective of the proposed F CAFT scheme is to sustain the performance of the network by timely healing the faults in the network, to enhance the durability and reliability of the WSN. The results of simulation and comparison with existing methods reveal that F CAFT is efficacious in enhancing the service lifetime of WSN by about 14% and sustains about 96% of coverage even when the failure rate is more than 20%, which is a necessity for critical monitoring and tracking applications of WSNs. [ABSTRACT FROM AUTHOR]

Past studies reveal the benefits of using Mobile Sink in Wireless Sensor Networks to bring about increased data collection efficiency and overall network performance in numerous applications. While several MS data gathering methods have been proposed, most of them are less adaptive to changes in network topology and fails to modify the MS path suitably in response to node failures. In this paper, we propose a Modified Sparrow Search Algorithm-based Mobile Sink Path Planning for WSNs (MSSPP) to create shorter travel route for MS and minimize data gathering latency. The proposed method helps in improving the performance of basic SSA by enhancing the quality of initial sparrow population, population diversity and search ability through modified strategies and is adaptive to node failure scenarios. In the first phase, we introduce a modified Sparrow Search-based algorithm to select a set of RPs that maximizes the coverage of nodes and minimizes the overlap in RP coverage. Then, an ACO-based path planning algorithm is utilized to determine the shortest tour through the RPs. The results reveal the effectiveness of MSSPP against other related approaches in terms of number of RPs, data gathering time, MS path, energy utilization and network lifetime. [ABSTRACT FROM AUTHOR]

One of the most important requirements for effective UAV–WSN operations is to perform data collection in timely and safe manner. Identifying an effective path in an environment with various obstacles and ensuring that the path may efficiently cover the selected stop points for effective data collection are both necessary and difficult. We propose a UAV-based effective data gathering scheme for wireless sensor networks with obstacles, where a UAV is employed as a mobile sink to collect data from the ground sensor nodes (EDGO). The main novelty includes a UAV–WSN collaborative approach for data gathering, which incorporates a convenient method for UAV trajectory design in a three-dimensional environment with obstacles. We propose an improved heuristic evolutionary approach based on genetic algorithm to determine the optimized trajectory for the UAV to gather data. In contrast to existing methods, the proposed method focus to reduce the length and angle cost of the path, minimize the energy consumption, and delay, and includes different evolutionary operations to generate a collision free path for UAV. Our approach retains the infeasible path through sufficient modifications, which improves the diversity of paths, so that it is possible to jump out of the local optima. The results reveal the effectiveness of the EDGO scheme against the other related approaches in terms of path cost and data collection efficiency. The network lifetime is extended by approximately 11% and offers a reduction of 42% and 35% in the UAV path length and travel time, respectively, when compared to the existing schemes. [ABSTRACT FROM AUTHOR]

The problem of Data acquisition in large distributed Wireless Sensor Networks (WSNs) scale is a hindrance in the growth of the Internet of Things (IoT). Recently, the combination of compressive sensing (CS) and routing techniques has attracted great interest from researchers. An open question of this approach is how to effectively integrate these technologies for specific tasks. The objective of this paper is two parts. First, we propose an effective deterministic clustering scheme based CS technique (EDCCS) for data collection in IoT based homogeneous and heterogeneous WSN to deal with the data acquisition problem, reduce the consumption of energy and increase the lifetime of network. Second, we propose random matching pursuit (RMP) as an effective CS reconstruction algorithm to improve the recovery process by reducing the error average at the base station (BS). The simulation results show that our proposed novel EDCCS scheme reduces at least 60% of the average power consumption and increases the network lifetime at least 1.3 times of the other schemes in homogeneous network while, it increases the network lifetime and residual energy by 1.9 times and 1.3 times respectively, compared to the other schemes in heterogeneous network. Also, our proposed RMP algorithm reduces the error average of reconstruction at least 35% compared to other reconstruction algorithms. [ABSTRACT FROM AUTHOR]

While several Mobile Sink (MS)-based data gathering methods have been proposed for Wireless Sensor Networks, most of them are less adaptive to changes in network topology, and the planned MS trajectory cannot be refined to accommodate node failures. Hence, a KD-Tree-based scheme (KDT) is proposed, which is an adaptive and robust algorithm that reduces network energy consumption and data gathering delay. In contrast to many existing algorithms, the number of Rendezvous Points (RPs) are assigned dynamically by prioritizing the nodes' coverage. Overlapping coverage of RPs is minimized, while guaranteeing 100% coverage of nodes. KDT is adaptive to network topology changes, and the planned MS trajectory can be refined to accommodate node failures. The shortest MS path is found using Ant Colony Optimization. Simulation results show that KDT requires approximately half the number of RPs and about 13% reduction in MS travel time compared to existing schemes. [ABSTRACT FROM AUTHOR]

An emergent solution to overcome the limitations of traditional multi-hop routing in wireless sensor networks (WSNs) is to use mobile collectors (MCs) for data gathering, thereby reducing energy consumed in internode communications. Most of the existing data collection approaches emphasize data gathering or network lifetime extension, without taking into account sensor node area coverage or how to handle sensor node failures through node mobility. It is desirable to utilize node mobility as a key functionality for WSN coverage optimization. We propose a robust coverage-aware multiple path-planning algorithm (CAMP) for WSN data gathering using MCs. CAMP works in tandem with any coverage hole-repair algorithm to heal coverage holes created by dying nodes, if any, and can plan efficient paths for MCs. CAMP initially selects polling points using Particle Swarm Optimization, and then divides the area into radial sections based on the number of available MCs. The size of subsection is adjusted to balance the estimated trip times within an acceptable margin and each MC traverses its assigned section following the shortest path determined by Ant Colony Optimization. Performance is analyzed in terms of coverage, energy consumption, data delivery delay, and network lifetime. Results reveal that CAMP provides above 90% coverage of nodes. Moreover, it is robust to failures and covers over 70% of the area even when more than half of the nodes fail. CAMP also saves a considerable amount of nodes' communication energy, and the network lifetime is increased by 2.5 times when compared to a similar state-of-the-art algorithm. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network with Mobile Collectors (MCs) enable increased flexibility and convenience in data gathering for numerous large-scale applications. However, introducing MCs also brings a new set of challenges to overcome. To reduce the data delivery latency of the application, it is required to select the minimum number of Rendezvous Points (RPs) that allow most sensors to forward data in single-hop and best path must be planned for each MC to provide uniform path length and round-trip time for all MCs. In contrast to existing schemes, we propose a Fuzzy C-Means based multi-layered RP Clustering and ACO-based Route-Planning scheme (FCM-RP) which is a robust method to determine RP-positions and MC-assignments. More particularly, the existing works have given priority to either reducing network energy consumption or minimizing data gathering delay. In our paper, along with these priorities, we consider the robustness and adaptability of the algorithm; meaning that, our algorithm can modify the planned trajectories of deployed MCs in response to sensor node failures. This allows to adapt to any changes in network topology caused by node failures or external factors. Also, to maximize utilization of each RP and provide greater coverage for nodes, the ideal positioning of RPs with minimum coverage overlap with neighboring RPs is considered. Simulation analysis shows that the FCM-RP scheme defines better routes for MCs in terms of total path length and delay, and significantly outperforms the existing algorithms in terms of total energy consumption and network lifetime. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) lend themselves to a wide variety of applications in our daily lives, such as environmental monitoring, safety, health-care, animal monitoring, etc. However, one of the key issues in WSN is energy constraints. This makes energy-conservation one of the major keys to the efficient functioning and lifetime of WSN. In this paper, given a network of nodes with heterogeneous energy, our goal is to determine energy-aware disjoint dominating sets (DSs) that work as data collection nodes in each round, to improve overall WSN lifetime. In order to accomplish this goal, we propose an intelligent data collection technique with two phases, the collector nodes selection, and the data gathering path formation and collection phases. In the collector nodes selection phase, an energy-aware algorithm based on swarm intelligence is proposed to construct disjoint dominating sets that work as collector nodes in each round. In the data gathering path formation and collection phase, data gathering path is determined for achieving maximal data collection efficiency and reduced energy consumption. The efficiency of our proposed technique is proved mathematically and through simulations. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) integrates diverse types of sensors, mobiles and other technologies to physical world and IoT technology is used in a wide range of applications. Compressive sensing based in-network compression is an efficient technique to reduce communication cost and accurately recover sensory data at the base station. In this paper, we investigate how compressive sensing can be combined with routing protocols for energy efficient data gathering in IoT-based wireless sensor networks. We propose a new compressive sensing routing scheme that includes the following new algorithms: (1) seed estimation algorithm to find the best measurement matrix by selecting the best-estimated seed, (2) chain construction algorithm to organize the network nodes during transmitting and receiving process, (3) compression approach to reduce the energy consumption and prolong the network lifetime by reducing the local data traffic, and (4) reconstruction algorithm to reconstruct the original data with minimum reconstruction error. The simulation results reveal that the proposed scheme outperforms existing baseline algorithms in terms of energy consumption, network lifetime and reconstruction error. [ABSTRACT FROM AUTHOR]

The resource-constrained nature of WSNs require efficient use of resources, especially energy, to prolong their lifetime. Clustering is one of the popular approaches to allocate the resources efficiently among the WSN nodes. In this work, we analyze the problem of round length determination in cluster based WSN which has severe impact on the energy efficiency. This problem is very important since round length determines how often the cluster head (CH) rotates or re-clustering process occurs. A longer round length will cause the CH nodes to operate for a long time and drain their energies faster than other nodes resulting in uneven energy consumption in the network, while a shorter round length results in considerable wastage of energy due to frequent running of the setup phase. Hence, we propose an adaptive and dynamic mechanism for round length determination in cluster based WSNs by adapting Behavior Curve Function modeled by quadratic Bezier curves, where we associate the remaining energy level of the cluster to its round operation length and to its assigned criticality which is defined based on network energy level. This helps to determine the number of frames in a round or how many times the data collection occurs in a cluster in a round and the criticality of the energy in the WSN. Simulation results reveal that the proposed mechanism has effectively reduced the energy consumption and improved the WSN lifetime in both homogeneous and heterogeneous network settings. [ABSTRACT FROM AUTHOR]

Providing effective sensing coverage of an observation area with reduced set of working nodes for maximum duration of time is an important concern for the development of durable and energy efficient WSN applications. A well-organized network structure can greatly promote such requirements. Motivated by the use of computational geometry in network design, we propose a coverage-aware and efficient planar face topology structure (CAFT) for WSN in this paper. Also, a distributed target tracking algorithm is proposed to run on the proposed face structure. Most of the existing works utilize the face based WSNs which are built by generating planarized graphs using Gabriel graph or Relative neighborhood graph in which all the deployed nodes become a part of the created toplogy. In contrast to this, our proposed distributed topology construction method selects and organizes a subset of nodes into faces, ensures coverage and connectivity while retaining the remaining nodes in sleep mode which can reduce redundant communication that may result in extra energy consumption and cost. The sleep nodes can promote durable service time for the WSN as such nodes can act as replacement nodes in case of node faults and failures, reducing coverage hole formation in the WSN, which is crucial in critical tracking applications. The simulation results and comparison with existing techniques prove that the proposed design is effective in reducing the energy consumption and thereby improves the WSN lifetime. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) consist of sensor nodes that have limited batteries, and are hard to replace due to their deployment in inaccessible areas. Hence, the goal of any WSN protocol is to implement energy efficient mechanisms that prolong the lifetime of WSNs. The existing face topology in WSNs can lead to high energy consumption due to all the nodes in the network being part of topology. In this paper, we propose a new protocol that introduces a new distributed energy-based face structure by putting redundant nodes to a sleep state for reduced energy consumption and prolonged lifetime of network. A distributed target tracking algorithm is designed to run on the new proposed face structure which tracks the target accurately while reducing the energy consumption of network. Additionally, a distributed fault tolerance algorithm is proposed which uses the sleeping redundant nodes in the network as backup nodes. When a node’s battery reaches a critical level it selects a nearby sleep node to replace itself to minimize the interruption in tracking of the target. The simulation results show that our proposed scheme achieves better energy efficiency and improves the lifetime of network while maintaining the performance of tracking. At high speeds, our work achieves significant improvement in accuracy of tracking. [ABSTRACT FROM AUTHOR]

Over the past few decades, one of the important advancements in wireless communication is low cost and limited power devices known as wireless sensor networks (WSNs). Sensor nodes are used to transmit data but have limited amount of energy. As the transmission takes place, energy gets depleted. So energy consumption and network lifetime are the major challenges in a WSN. Much research has been done in the past years to determine an optimal path between source and destination nodes, which will result in maximizing energy conservation of a network. However, the challenge is to create a routing algorithm that takes into consideration the major issues of minimizing energy consumption and maximizing network lifetime. Various optimization techniques are available to determine a routing path between a source node and destination node. In this article, we look into the details of routing in WSN using different optimization techniques. This article provides us a comprehensive summary of the previous studies in field of WSN during the span of 2010–2019. The results provided in this article provide the future insight for researchers to fill in existing gaps in the WSN research field and to find new research trends in this area. [ABSTRACT FROM AUTHOR]

This paper proposes a novel dynamic, distributive, and self-organizing entropy based clustering scheme that benefits from the local information of sensor nodes measured in terms of entropy and use that as criteria for cluster head election and cluster formation. It divides the WSN into two-levels of hierarchy and three-levels of energy heterogeneity of sensor nodes. The simulation results reveal that the proposed approach outperforms existing baseline algorithms in terms of energy consumption, stability period, and the network lifetime. [ABSTRACT FROM AUTHOR]

The work of wireless sensor networks (WSNs) depends on reliable coverage of the area to be monitored. The problem of coverage holes arises when one or more nodes fail due to energy depletion or harsh physical environments. Furthermore, random deployment of nodes could lead to a high degree of overlapping coverage among the sensor nodes. While there are many research papers on optimal deployment of sensor nodes, coverage holes that appear post-deployment are not often considered. In this paper, we propose an algorithm that employs adjustable sensing ranges and exploits node mobility to repair emergent coverage holes. The algorithm selects suitable nodes by gauging the degree of overlap and the residual energy of each node in the vicinity of the coverage hole. We have evaluated the performance of the algorithm via simulation, and compared with baseline approaches in terms of coverage performance and energy cost. Simulations have shown that our approach outperforms others in terms of coverage and network lifetime. [ABSTRACT FROM AUTHOR]

Wireless multimedia sensor networks (WMSNs) are receiving increasing consideration in the deployment of unmanned surveillance systems for applications such as monitoring battle field and security surveillance. However, ensuring high levels of coverage of the monitored area and concurrently maximising the WMSN lifetime remains a challenging issue. The authors introduce a novel distributed algorithm in this study, for scheduling multimedia sensor nodes (MSNs) activity that maximises coverage and minimises energy consumption. The proposed activity scheduling algorithm incorporates an approach that enables each MSN in WMSN to efficiently extract its cover sets to ensure that the critical points are monitored, and thereby minimise and effectively balance the network energy utilisation. They introduce a new way to select the most suitable cover set of an MSN by assigning priority for each cover set based on the correlation of visual information from the camera observations. The efficacy of the proposed approach is studied through rigorous simulations. Their simulation work and results comparison with existing approaches demonstrate that their proposed scheme exceeds current results in terms of network lifetime, capture rate and percentages of dead and active nodes. [ABSTRACT FROM AUTHOR]

Abstract Several studies have demonstrated the benefits of using a mobile sink (MS) to reduce energy consumption resulting from multi-hop data collection using a static sink in wireless sensor networks (WSNs). However, using MS may increase data delivery latency as it needs to visit each sensor node in the network to collect data. This is a critical issue in delay-sensitive applications where all sensed data must be gathered within a given time constraint. In this paper, we propose a distributed data gathering protocol utilizing MS for WSNs. The proposed protocol designs a trajectory for the MS, which minimizes energy consumption and delay. Our protocol operates in four main phases: data sensing, rendezvous point (RP) selection, trajectory design, and data gathering. In data sensing, a number of deployed sensor nodes keep sensing the target field for a specific period of time to capture events. Then, using a cluster-based RP selection algorithm, some sensor nodes are selected to become RPs based on local information. The selected RPs are then used to determine a trajectory for the MS. To do so, we propose three trajectory design algorithms that support different types of applications, namely reduced energy path (REP), reduced delay path (RDP), and delay bound path (DBP). The MS moves through the constructed path to accomplish its data gathering according to an effective scheduling technique that is introduced in this work. We validate the proposed protocol via extensive simulations over several metrics such as energy, delay, and time complexity. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSN), most of the attention has been given to the routing protocols since they might differ depending on the application and network architecture. It has been seen from existing hierarchical routing protocols that most of them ensure longer lifetime of the network. However, one of the major challenges in WSN is to prolong the time interval before the death of first node. It can be referred as stability period. Without longer stability period, more information could not be collected from the sensor field even though the lifetime of the network is high. So prolonging the stability period is crucial for many applications. In this paper, the cluster routing protocol SEP (Stable Election Protocol) is considered and improved. We propose a clustering routing protocol named SEP-CS that combines new clustering strategy with compressive sensing (CS) theory to satisfy both energy and stability period constraints under heterogeneous environment in WSNs. The proposed protocol efficiently improves data aggregation and therefore significantly reduces the energy consumed in the process of sampling and transmission, and lowers the wireless bandwidth required for communication. Simulation results show that our improved protocol perfonns better than the existing protocols in terms of stability period, network lifetime, and network throughout. [ABSTRACT FROM AUTHOR]

Abstract: All properties of mobile wireless sensor networks (MWSNs) are inherited from static wireless sensor networks (WSNs) and meanwhile have their own uniqueness and node mobility. Sensor nodes in these networks monitor different regions of an area of interest and collectively present a global overview of monitored activities. Since failure of a sensor node leads to loss of connectivity, it may cause a partitioning of the network. Adding mobility to WSNs can significantly increase the capability of the WSN by making it resilient to failures, reactive to events, and able to support disparate missions with a common set of sensor nodes. In this paper, we propose a new algorithm based on the divide-and-conquer approach, in which the whole region is divided into sub-regions and in each sub-region the minimum connected sensor cover set is selected through energy-aware selection method. Also, we propose a new technique for mobility assisted minimum connected sensor cover considering the network energy. We provide performance metrics to analyze the performance of our approach and the simulation results clearly indicate the benefits of our new approach in terms of energy consumption, communication complexity, and number of active nodes over existing algorithms. [Copyright &y& Elsevier]

Abstract: Target tracking is an important sensing application of wireless sensor networks. In these networks, energy, computing power, and communication bandwidth are scarce. We have considered a random heterogeneous wireless sensor network, which has several powerful nodes for data aggregation/relay and large number of energy-constrained sensor nodes that are deployed randomly to cover a given target area. In this paper, a cooperative approach to detect and monitor the path of a moving object using a minimum subset of nodes while maintaining coverage and network connectivity is proposed. It is tested extensively in a simulation environment and compared with other existing methods. The results of our experiments clearly indicate the benefits of our new approach in terms of energy consumption. [Copyright &y& Elsevier]

The most fundamental task of the wireless sensor network (WSN) is to monitor a specified region of interest with sufficient sensor coverage. This task is jeopardized when coverage holes appear after the network's deployment. The emergence of coverage holes is unavoidable for many reasons such as sensor node energy depletion, physical damage, or external attacks. Therefore, it is important to have an ongoing mechanism for coverage maintenance because coverage holes can have debilitating effects on network performance if left unattended. Existing research into coverage hole repair tend to suffer from several shortcomings such as high energy consumption of mobile nodes and inaccurate assumptions about the coverage hole model. Furthermore, many solutions rely on the availability of redundant nodes or deploying new nodes in the network, which is not always feasible. We present a novel distributed self-healing algorithm called distributed hole detection and repair (DHDR), that simultaneously handles both hole detection and repair using only nodes already deployed in the network. Our algorithm can dynamically detect a coverage hole as it occurs, and accurately estimate its position and size. The algorithm selects suitable nodes from the vicinity which will maximize coverage and minimize energy consumption by sharing information and coordinating their movements. The selected nodes relocate in a way that restores the void area of a hole without disrupting their existing coverage or connectivity. The performance of the proposed approach is evaluated through simulation experiments. It is compared against two state-of-the-art algorithms and has been shown to outperform both in terms of improved coverage, more stable connectivity, and lower energy consumption. [ABSTRACT FROM AUTHOR]

With the emergence of Internet of Things (IoT), the research on Smart Cities with wireless sensor networks (WSNs) got leveraged due to similarities between objectives in both Smart City and IoT. Along with them, research in controlling WSN faces new challenges and opportunities for data aggregation and routing has received consistent focus from researchers. Yet new techniques are being proposed to address modern challenges in WSN and efficient resource utilization. Moreover, solutions are required to integrate existing deployed WSN with ever increasing numbers of IoT devices in Smart Cities, that benefit both. In this work, we present an approach for routing in a WSN, in which IoT is used opportunistically to reduce the communication overhead of the sensors. In our approach, WSN deployed in a Smart City interacts with the IoT devices to route the data to the sink. We build a prototype Integration Platform for the WSN that allows interaction with IoT devices and utilizes them opportunistically that results in an energy efficient routing of data. Simulation results show that the direction is quite promising and our approach offers to utilize IoT to gain unique advantages. [ABSTRACT FROM AUTHOR]