An energy-balanced swept-coverage mechanism for mobile WSNs

Coverage is one of the most important issues in Wireless Sensor Networks (WSNs). In literature, many coverage mechanisms have been proposed and employed mobile sensors to cover (heal) the coverage holes in the monitoring region. Consider that there are no redundant mobile sensors in a monitoring region. Some studies presented hole-movement mechanisms which used a mobile sensor to move a hole from one location to another, achieving the swept coverage of the monitoring region. However, in these studies, there are only some mobile sensors that participate in the hole-movement task, leading to an energy-unbalanced WSN. This paper considers a mobile WSN that contains holes and has no redundant mobile sensors to reach the spatial full coverage of the given monitoring region. To meet the swept coverage of the given monitoring region and balance the energies of mobile sensors, a distributed energy-balanced hole-movement mechanism, called EBHMM, is proposed. Theoretical analysis and performance evaluation reveal that the proposed EBHMM has better performance than existing hole-movement mechanisms in terms of the network lifetime and energy-balanced degree of mobile sensors.     

An energy‐efficient hole‐healing mechanism for wireless sensor networks with obstacles

In wireless sensor networks (WSNs), coverage of the monitoring area represents the surveillance quality. Since sensor nodes are battery powered and placed outdoor, there will be failures due to energy exhaustion or environmental influence, resulting in coverage‐loss. In literature, a number of studies developed robot repairing algorithms that aim at maintaining full coverage. However, they did not consider the time constraint for network maintenance. Furthermore, they did not consider the existence of obstacles and the constraint of limited energy of the robot. This paper presents a novel tracking mechanism and robot repairing algorithm for maintaining the coverage quality of the given WSN. Without support of location information, the tracking mechanism leaves robot's footmark on sensors so that they can learn better routes for sending repairing requests to the robot. Upon receiving several repairing request messages, the robot applies the proposed repairing algorithm to establish an efficient route that passes through all failure regions with low overhead in terms of the required time and the power consumption. In addition, the proposed repairing algorithm also considers the remaining energy of the robot so that the robot can move back to home for recharging energy and overcome the unpredicted obstacles. Performance results reveal that the developed protocol can efficiently maintain the coverage quality while the required time and energy consumption are significantly reduced. Copyright © 2011 John Wiley & Sons, Ltd.     

An integrated framework for wireless sensor network management

Wireless sensor networks (WSNs) have significant potential in many application domains, ranging from precision agriculture and animal welfare to home and office automation. Although sensor network deployments have only begun to appear, the industry still awaits the maturing of this technology to realize its full benefits. The main constraints to large‐scale commercial adoption of WSN have been the lack of available network management and control tools, such as for determining the degree of data aggregation prior to transforming it into useful information, localizing the sensors accurately so that timely emergency actions can be taken at an exact location, routing data by reducing sensor energy consumption, and scheduling data packets so that data are sent according to their priority and fairness. Moreover, to the best of our knowledge, no integrated network management solution comprising efficient localization, data scheduling, routing, and data aggregation approaches exists in the literature for a large‐scale WSN. Thus, we introduce an integrated network management framework comprising sensor localization, routing, data scheduling, and data aggregation for a large‐scale WSN. Experimental results show that the proposed framework outperforms an existing approach that comprises only localization and routing protocols in terms of localization energy consumption, localization error, end‐to‐end delay, packet loss ratio, and network energy consumption. Moreover, the proposed WSN management framework has potential in building a future “Internet of Things”. Copyright © 2012 John Wiley & Sons, Ltd.     

Accuracy,latency, and energy cross‐optimization in wireless sensor networks through infection spreading

In this paper, cross‐optimization of accuracy, latency, and energy in wireless sensor networks (WSNs) through infection spreading is investigated. Our solution is based on a dual‐layer architecture for efficient data harvesting in a WSN, in which, the lower layer sensors are equipped with a novel adaptive data propagation method inspired by infection spreading and the upper layer consists of randomly roaming data harvesting agents. The proposed infection spreading mechanisms, namely random infection (RI) and linear infection (LI), are implemented at the lower layer. The entire sensor field is dynamically separated into several busy areas (BA) and quiet areas (QA). According to the BA or QA classification, the level of importance is defined, on which, the optimal number of infections for a particular observation is evaluated. Therefore, the accessed probability for observations with a relatively higher importance level is adaptively increased. The proposed mechanisms add further value to the data harvesting operation by compensating for its potential lack of coverage due to random mobility and tolerable delay, thus a relatively higher accuracy and latency requirements can be guaranteed for the optimization of energy consumption in a dynamically changing environment. Further, with the cost of processing simple location information, LI is proved to outperform RI. Copyright © 2010 John Wiley & Sons, Ltd.     

e‐LiteSense: Self‐adaptive energy‐aware data sensing in WSN environments

Currently deployed in a wide variety of applicational scenarios, wireless sensor networks (WSNs) are typically a resource‐constrained infrastructure. Consequently, characteristics such as WSN adaptability, low‐overhead, and low‐energy consumption are particularly relevant in dynamic and autonomous sensing environments where the measuring requirements change and human intervention is not viable. To tackle this issue, this article proposes e‐LiteSense as an adaptive, energy‐aware sensing solution for WSNs, capable of auto‐regulate how data are sensed, adjusting it to each applicational scenario. The proposed adaptive scheme is able to maintain the sensing accuracy of the physical phenomena, while reducing the overall process overhead. In this way, the adaptive algorithm relies on low‐complexity rules to establish the sensing frequency weighting the recent drifts of the physical parameter and the levels of remaining energy in the sensor. Using datasets from WSN operational scenarios, we prove e‐LiteSense effectiveness in self‐regulating data sensing accurately through a low‐overhead process where the WSN energy levels are preserved. This constitutes a step‐forward for implementing self‐adaptive energy‐aware data sensing in dynamic WSN environments.     

Effect of realistic sea surface movements in achieving full‐view coverage camera sensor network

In stationary camera sensor networks (CSNs), when the deployment characteristics and sensing models are defined, the coverage can be deduced and remain unchanged over time. However, in the maritime environment, the rough and random sea condition can move CSN from the initial location. We envisage that camera sensors are mounted on quasi‐mobile platforms such as buoys. Hence, it is important to understand the effect of realistic sea surface movements in achieving full‐view coverage because in full‐view coverage, target's facing direction is taken into account to judge whether a target is guaranteed to be captured because image shot at the frontal viewpoint of a given target considerably increases the possibility to detect and recognize the target. To accurately emulate the maritime environment, the movement of the buoy, which is attached with a cable that is nailed at the sea floor, has been characterized based on the sea wave that is created by the wind, and it is limited by the cable. The average percentage of full‐view coverage has been evaluated based on different parameters such as equilateral triangle grid length, sensing radius of camera, wind speed and wave height. Furthermore, a method to improve the target detection and recognition has been proposed in the presence of poor link quality using cooperative transmission with low power consumption. In some parameter scenario, the cooperative transmission method has achieved around 70% improvement in the average percentage of full‐view coverage of a given target and total reduction of around 13% for the total transmission power P_Total(Q). Copyright © 2015 John Wiley & Sons, Ltd.     

Efficient flow‐control algorithm cooperating with energy allocation scheme for solar‐powered WSNs

Recently, solar energy emerged as a feasible supplement to battery power for wireless sensor networks (WSNs) which are expected to operate for long periods. Since solar energy can be harvested periodically and permanently, solar‐powered WSNs can use the energy more efficiently for various network‐wide performances than traditional battery‐based WSNs of which aim is mostly to minimize the energy consumption for extending the network lifetime. However, using solar power in WSNs requires a different energy management from battery‐based WSNs since solar power is a highly varying energy supply. Therefore, firstly we describe a time‐slot‐based energy allocation scheme to use the solar energy optimally, based on expectation model for harvested solar energy. Then, we propose a flow‐control algorithm to maximize the amount of data collected by the network, which cooperates with our energy allocation scheme. Our algorithms run on each node in a distributed manner using only local information of its neighbors, which is a suitable approach for scalable WSNs. We implement indoor and outdoor testbeds of solar‐powered WSN and demonstrate the efficiency of our approaches on them. Copyright © 2010 John Wiley & Sons, Ltd.     

Prediction‐based cluster management for target tracking in wireless sensor networks

The key impediments to a successful wireless sensor network (WSN) application are the energy and the longevity constraints of sensor nodes. Therefore, two signal processing oriented cluster management strategies, the proactive and the reactive cluster management, are proposed to efficiently deal with these constraints. The former strategy is designed for heterogeneous WSNs, where sensors are organized in a static clustering architecture. A non‐myopic cluster activation rule is realized to reduce the number of hand‐off operations between clusters, while maintaining desired estimation accuracy. The proactive strategy minimizes the hardware expenditure and the total energy consumption. On the other hand, the main concern of the reactive strategy is to maximize the network longevity of homogeneous WSNs. A Dijkstra‐like algorithm is proposed to dynamically form active cluster based on the relation between the predictive target distribution and the candidate sensors, considering both the energy efficiency and the data relevance. By evenly distributing the energy expenditure over the whole network, the objective of maximizing the network longevity is achieved. The simulations evaluate and compare the two proposed strategies in terms of tracking accuracy, energy consumption and execution time. Copyright © 2010 John Wiley & Sons, Ltd.     

A gradient‐based multiple‐path routing protocol for low duty‐cycled wireless sensor networks

Routing in a low duty‐cycled wireless sensor network (WSN) has attracted much attention recently because of the challenge that low duty‐cycled sleep scheduling brings to the design of efficient distributed routing protocols for such networks. In a low duty‐cycled WSN, a big problem is how to design an efficient distributed routing protocol, which uses only local network state information while achieving low end‐to‐end (E2E) packet delivery delay and also high packet delivery efficiency. In this paper, we study low duty‐cycled WSNs wherein sensor nodes adopt pseudorandom sleep scheduling for energy saving. The objective of this paper is to design an efficient distributed routing protocol with low overhead. For this purpose, we design a simple but efficient hop‐by‐hop routing protocol, which integrates the ideas of multipath routing and gradient‐based routing for improved routing performance. We conduct extensive simulations, and the results demonstrate the high performance of the proposed protocol in terms of E2E packet delivery latency and packet delivery efficiency as compared with existing protocols. Copyright © 2014 John Wiley & Sons, Ltd.     

Enhancing Coverage Using Weight Based Clustering in Wireless Sensor Networks

Energy conservation in wireless sensor networks (WSNs) is a fundamental issue. For certain surveillance applications in WSN, coverage lifetime is an important issue and this is related to energy consumption significantly. In order to handle these two interlinked aspects in WSN, a new scheme named Weight based Coverage Enhancing Protocol (WCEP) has been introduced. The WCEP aims to obtain longer full coverage and better network life time. The WCEP is based on assigning different weight values to certain governing parameters which are residual energy, overlapping degree, node density and degree of sensor node. These governing parameters affect the energy and coverage aspects predominantly. Further, these four different parameters are prime elements in cluster formation process and node scheduling mechanisms. The weight values help in selection of an optimal group of Cluster Heads and Cluster Members, which result in enhancement of complete coverage lifetime. The simulation results indicate that WCEP performs better in terms of energy consumption also. The enhancement of value 24% in full coverage lifetime has been obtained as compared to established existing techniques.

     

Securing Wireless Sensor Networks Against Denial‐of‐Sleep Attacks Using RSA Cryptography Algorithm and Interlock Protocol

Wireless sensor networks (WSNs) have been vastly employed in the collection and transmission of data via wireless networks. This type of network is nowadays used in many applications for surveillance activities in various environments due to its low cost and easy communications. In these networks, the sensors use a limited power source which after its depletion, since it is non‐renewable, network lifetime ends. Due to the weaknesses in sensor nodes, they are vulnerable to many threats. One notable attack threating WSN is Denial of Sleep (DoS). DoS attacks denotes the loss of energy in these sensors by keeping the nodes from going into sleep and energy‐saving mode. In this paper, the Abnormal Sensor Detection Accuracy (ASDA‐RSA) method is utilized to counteract DoS attacks to reducing the amount of energy consumed. The ASDA‐RSA schema in this paper consists of two phases to enhancement security in the WSNs. In the first phase, a clustering approach based on energy and distance is used to select the proper cluster head and in the second phase, the RSA cryptography algorithm and interlock protocol are used here along with an authentication method, to prevent DoS attacks. Moreover, ASDA‐RSA method is evaluated here via extensive simulations carried out in NS‐2. The simulation results indicate that the WSN network performance metrics are improved in terms of average throughput, Packet Delivery Ratio (PDR), network lifetime, detection ratio, and average residual energy.     

Efficient Approach for Maximizing Lifespan in Wireless Sensor Networks by Using Mobile Sinks

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster‐head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well‐known algorithms.     

A secure and low‐energy zone‐based wireless sensor networks routing protocol for pollution monitoring

Sensor networks can be used in many sorts of environments. The increase of pollution and carbon footprint are nowadays an important environmental problem. The use of sensors and sensor networks can help to make an early detection in order to mitigate their effect over the medium. The deployment of wireless sensor networks (WSNs) requires high‐energy efficiency and secures mechanisms to ensure the data veracity. Moreover, when WSNs are deployed in harsh environments, it is very difficult to recharge or replace the sensor's batteries. For this reason, the increase of network lifetime is highly desired. WSNs also work in unattended environments, which is vulnerable to different sort of attacks. Therefore, both energy efficiency and security must be considered in the development of routing protocols for WSNs. In this paper, we present a novel Secure and Low‐energy Zone‐based Routing Protocol (SeLeZoR) where the nodes of the WSN are split into zones and each zone is separated into clusters. Each cluster is controlled by a cluster head. Firstly, the information is securely sent to the zone‐head using a secret key; then, the zone‐head sends the data to the base station using the secure and energy efficient mechanism. This paper demonstrates that SeLeZoR achieves better energy efficiency and security levels than existing routing protocols for WSNs. Copyright © 2016 John Wiley & Sons, Ltd.     

Affinity propagation‐based interference‐free clustering for wireless sensor networks

Wireless sensor networks (WSNs), eg, industrial WSNs, require reliability and real‐time communication. Clustering technique together with schedule‐based access can provide the benefits, such as energy saving, reliability, and timeliness. However, integrating above two technologies into WSNs requires sophistical time slot allocation mechanism. To simplify the time slot allocation, the paper proposes a distributed interference‐free clustering algorithm for WSNs. The algorithm is inspired by affinity propagation (AP) clustering algorithm. By adapting and improving the original AP algorithm, the proposed clustering algorithm aims to jointly optimize energy saving and coverage issues while providing interference free between clusters. The performance analysis demonstrates that it can achieve high receiving rate (reliability), low delay (real time), and low‐energy consumption.     

A distributed activity scheduling algorithm for wireless sensor networks with partial coverage

One of the most important design objectives in wireless sensor networks (WSN) is minimizing the energy consumption since these networks are expected to operate in harsh conditions where the recharging of batteries is impractical, if not impossible. The sleep scheduling mechanism allows sensors to sleep intermittently in order to reduce energy consumption and extend network lifetime. In applications where 100% coverage of the network field is not crucial, allowing the coverage to drop below full coverage while keeping above a predetermined threshold, i.e., partial coverage, can further increase the network lifetime. In this paper, we develop the distributed adaptive sleep scheduling algorithm (DASSA) for WSNs with partial coverage. DASSA does not require location information of sensors while maintaining connectivity and satisfying a user defined coverage target. In DASSA, nodes use the residual energy levels and feedback from the sink for scheduling the activity of their neighbors. This feedback mechanism reduces the randomness in scheduling that would otherwise occur due to the absence of location information. The performance of DASSA is compared with an integer linear programming (ILP) based centralized sleep scheduling algorithm (CSSA), which is devised to find the maximum number of rounds the network can survive assuming that the location information of all sensors is available. DASSA is also compared with the decentralized DGT algorithm. DASSA attains network lifetimes up to 92% of the centralized solution and it achieves significantly longer lifetimes compared with the DGT algorithm.     

An asynchronous low‐power medium access control protocol for wireless sensor networks

Duty cycling is a fundamental approach used in contention‐based medium access control (MAC) protocols for wireless sensor networks (WSNs) to reduce power consumption in sensor nodes. Existing duty cycle‐based MAC protocols use either scheduling or low‐power listening (LPL) to reduce unnecessary energy lost caused by idle listening and overhearing. This paper presents a new asynchronous duty‐cycled MAC protocol for WSN. It introduces a novel dual preamble sampling (DPS) approach to efficiently coordinate channel access among nodes. DPS combines LPL with a short‐strobed preamble approach to significantly reduce the idle‐listening issue in existing asynchronous protocols. We provide detailed analysis of the energy consumption by using well‐known energy models and compare our work with B‐MAC and X‐MAC, two most popular asynchronous duty cycle‐based MAC protocols for WSNs. We also present experimental results based on NS‐2 simulations. We show that depending on the traffic load and preamble length, the proposed MAC protocol improves energy consumption significantly without degrading network performances in terms of delivery ratio and latency. For example, for a traffic rate of 0.1 packets/s and a preamble length of 0.1 s, the average improvement in energy consumption is about 154%. Copyright © 2011 John Wiley & Sons, Ltd.     

Distributed fuzzy logic based energy‐aware and coverage preserving unequal clustering algorithm for wireless sensor networks

In an energy‐constrained wireless sensor networks (WSNs), clustering is found to be an effective strategy to minimize the energy depletion of sensor nodes. In clustered WSNs, network is partitioned into set of clusters, each having a coordinator called cluster head (CH), which collects data from its cluster members and forwards it to the base station (BS) via other CHs. Clustered WSNs often suffer from the hot spot problem where CHs closer to the BS die much early because of high energy consumption contributed by the data forwarding load. Such death of nodes results coverage holes in the network very early. In most applications of WSNs, coverage preservation of the target area is a primary measure of quality of service. Considering the energy limitation of sensors, most of the clustering algorithms designed for WSNs focus on energy efficiency while ignoring the coverage requirement. In this paper, we propose a distributed clustering algorithm that uses fuzzy logic to establish a trade‐off between the energy efficiency and coverage requirement. This algorithm considers both energy and coverage parameters during cluster formation to maximize the coverage preservation of target area. Further, to deal with hot spot problem, it forms unequal sized clusters such that more CHs are available closer to BS to share the high data forwarding load. The performance of the proposed clustering algorithm is compared with some of the well‐known existing algorithms under different network scenarios. The simulation results validate the superiority of our algorithm in network lifetime, coverage preservation, and energy efficiency.     

GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs

A wireless sensor network (WSN) is a prominent technology that could assist in the fourth industrial revolution. Sensor nodes present in the WSNs are functioned by a battery. It is impossible to recharge or replace the battery, hence energy is the most important resource of WSNs. Many techniques have been devised and used over the years to conserve this scarce resource of WSNs. Clustering has turned out to be one of the most efficient methods for this purpose. This paper intends to propose an efficient technique for election of cluster heads in WSNs to increase the network lifespan. For the achievement of this task, grey wolf optimizer (GWO) has been employed. In this paper, the general GWO has been modified to cater to the specific purpose of cluster head selection in WSNs. The objective function for the proposed formulation considers average intra‐cluster distance, sink distance, residual energy, and CH balancing factor. The simulations are carried out in diverse conditions. On comparison of the proposed protocol, ie, GWO‐C protocol with some well‐known clustering protocols, the obtained results prove that the proposed protocol outperforms with respect to the consumption of the energy, throughput, and the lifespan of the network. The proposed protocol forms energy‐efficient and scalable clusters.     

Energy‐efficient data transmission technique for wireless sensor networks based on DSC and virtual MIMO

In a wireless sensor network (WSN), the data transmission technique based on the cooperative multiple‐input multiple‐output (CMIMO) scheme reduces the energy consumption of sensor nodes quite effectively by utilizing the space‐time block coding scheme. However, in networks with high node density, the scheme is ineffective due to the high degree of correlated data. Therefore, to enhance the energy efficiency in high node density WSNs, we implemented the distributed source coding (DSC) with the virtual multiple‐input multiple‐output (MIMO) data transmission technique in the WSNs. The DSC‐MIMO first compresses redundant source data using the DSC and then sends it to a virtual MIMO link. The results reveal that, in the DSC‐MIMO scheme, energy consumption is lower than that in the CMIMO technique; it is also lower in the DSC single‐input single‐output (SISO) scheme, compared to that in the SISO technique at various code rates, compression rates, and training overhead factors. The results also indicate that the energy consumption per bit is directly proportional to the velocity and training overhead factor in all the energy saving schemes.