Wireless communication systems, such as wireless sensor networks and RFIDs, are increasingly adopted to transfer potential highly sensitive information. Since the wireless medium has a sharing nature, adversaries have a chance to eavesdrop confidential information from the communication systems. Adding artificial noises caused by friendly jammers emerges as a feasible defensive technique against adversaries. This paper studies the schedule strategies of friendly jammers, which are randomly and redundantly deployed in a circumscribed geographical area and can be unrechargeable or rechargeable, to maximize the lifetime of the jammer networks and prevent the cracking of jamming effect made by the eavesdroppers, under the constraints of geographical area, energy consumption, transmission power, and threshold level. An approximation algorithm as baseline is first proposed using the integer linear programming model. To further reduce the computational complexity, a heuristic algorithm based on the greedy strategy that less consumption leads to longer lifetime is also proposed. Finally, extensive simulation results show that the proposed algorithms are effective and efficient.
The issue of extending operational time of battery-powered wireless sensor networks was investigated in @cite_13 . The basic idea is to first organize sensors into a maximal number of disjoint set covers, and then activate these sets in turn successively to monitor all targets. A heuristic algorithm based on mixed integer programming was then proposed to compute the sets. This work is on target monitoring by sensors, not on communication protecting with friendly jammers. Moreover, the assumption that the locations of all targets are known in advance is unrealistic in protecting communication with friendly jammers since the locations of the eavesdroppers are not fixed and often unknown. Battery-powered rechargeable networks have also drawn a lot of attention among researchers @cite_20 @cite_6 @cite_16 @cite_25 @cite_26 @cite_14 . Although these works mainly focused on energy harvesting in sensor networks, they provided a new perspective for the security of wireless communication by rechargeable jammers.
@cite_26 @cite_14 @cite_6 @cite_16 @cite_13 @cite_25 @cite_20
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A critical aspect of applications with wireless sensor networks is network lifetime. Battery-powered sensors are usable as long as they can communicate captured data to a processing node. Sensing and communications consume energy, therefore judicious power management and scheduling can effectively extend operational time. To monitor a set of targets with known locations when ground access in the monitored area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The loss of precise sensor placement would then be compensated by a large sensor population density in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node for processing. In this paper we propose an efficient method to extend the sensor network operational time by organizing the sensors into a maximal number of disjoint set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while nodes from all other sets are in a low-energy sleep mode. In this paper we address the maximum disjoint set covers problem and we design a heuristic that computes the sets. Theoretical analysis and performance evaluation results are presented to verify our approach. The emerging wireless energy transfer technology enables charging sensor batteries in a wireless sensor network (WSN) and maintaining perpetual operation of the network. Recent breakthrough in this area has opened up a new dimension to the design of sensor network protocols. In the meanwhile, mobile data gathering has been considered as an efficient alternative to data relaying in WSNs. However, time variation of recharging rates in wireless rechargeable sensor networks imposes a great challenge in obtaining an optimal data gathering strategy. In this paper, we propose a framework of joint Wireless Energy Replenishment and anchor-point based Mobile Data Gathering (WerMDG) in WSNs by considering various sources of energy consumption and time-varying nature of energy replenishment. To that end, we first determine the anchor point selection and the sequence to visit the anchor points. We then formulate the WerMDG problem into a network utility maximization problem which is constrained by flow conversation, energy balance, link and battery capacity and the bounded sojourn time of the mobile collector. Furthermore, we present a distributed algorithm composed of cross-layer data control, scheduling and routing subalgorithms for each sensor node, and sojourn time allocation subalgorithm for the mobile collector at different anchor points. Finally, we give extensive numerical results to verify the convergence of the proposed algorithm and the impact of utility weight on network performance. The application of wireless charging technology in traditional battery-powered wireless sensor networks (WSNs) grows rapidly recently. Although previous studies indicate that the technology can deliver energy reliably, it still faces regulatory mandate to provide high power density without incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high energy demands from cluster heads and the relatively low energy supplies from wireless chargers. Fortunately, solar energy harvesting can provide high power density without health risks. However, its reliability is subject to weather dynamics. In this paper, we propose a hybrid framework that combines the two technologies - cluster heads are equipped with solar panels to scavenge solar energy and the rest of nodes are powered by wireless charging. We divide the network into three hierarchical levels. On the first level, we study a discrete placement problem of how to deploy solar-powered cluster heads that can minimize overall cost and propose a distributed @math -approximation algorithm for the placement. Then, we extend the discrete problem into continuous space and develop an iterative algorithm based on the Weiszfeld algorithm. On the second level, we establish an energy balance in the network and explore how to maintain such balance for wireless-powered nodes when sunlight is unavailable. We also propose a distributed cluster head re-selection algorithm. On the third level, we first consider the tour planning problem by combining wireless charging with mobile data gathering in a joint tour. We then propose a polynomial-time scheduling algorithm to find appropriate hitting points on sensors’ transmission boundaries for data gathering. For wireless charging, we give the mobile chargers more flexibility by allowing partial recharge when energy demands are high. The problem turns out to be a Linear Program. By exploiting its particular structure, we propose an efficient algorithm that can achieve near-optimal solutions. Our extensive simulation results demonstrate that the hybrid framework can reduce battery depletion by 20 percent and save vehicles’ moving cost by 25 percent compared to previous works. By allowing partial recharge, battery depletion can be further reduced at a slightly increased cost. The results also suggest that we can reduce the number of high-cost mobile chargers by deploying more low-cost solar-powered sensors. This paper addresses the coverage breach problem in wireless sensor networks with limited bandwidths. In wireless sensor networks, sensor nodes are powered by batteries. To make efficient use of battery energy is critical to sensor network lifetimes. When targets are redundantly covered by multiple sensors, especially in stochastically deployed sensor networks, it is possible to save battery energy by organizing sensors into mutually exclusive subsets and alternatively activating only one subset at any time. Active nodes are responsible for sensing, computing and communicating. While the coverage of each subset is an important metric for sensor organization, the size of each subset also plays an important role in sensor network performance because when active sensors periodically send data to base stations, contention for channel access must be considered. The number of available channels imposes a limit on the cardinality of each subset. Coverage breach happens when a subset of sensors cannot completely cover all the targets. To make efficient use of both energy and bandwidth with a minimum coverage breach is the goal of sensor network design. This paper presents the minimum breach problem using a mathematical model, studies the computational complexity of the problem, and provides two approximate heuristics. Effects of increasing the number of channels and increasing the number of sensors on sensor network coverage are studied through numerical simulations. Overall, the simulation results reveal that when the number of sensors increases, network lifetimes can be improved without loss of network coverage if there is no bandwidth constraint; with bandwidth constraints, network lifetimes may be improved further at the cost of coverage breach.