1522 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 20, NO. 6, NOVEMBER 2012 An Ef ficient Target Monitoring Scheme With Controlled Node Mobility for Sensor Networks Hamid Mahboubi, Student Member, IEEE, Ahmadreza Momeni, Member, IEEE, Amir G. Aghdam, Senior Member, IEEE, Kamran Sayrafian-Pour, Senior Member, IEEE, and Vladimir Marbukh, Senior Member, IEEE Abstract—This paper is concerned with target monitoring using a network of collaborative mobile sensors. The objective is to com- pute (online) the desired sensing and communication radii of sen- sors as well as their location at each time instant, such that a set of prescribed specifications are met. These specifications include end-to-end connectivity preservation from the target to a fixed des- tination, while durability of sensors is maximized and the overall energy consumption is minimized. The problem is formulated as a constrained optimization, and a procedure is presented to solve it. Simulation results demonstrate the effectiveness of the proposed techniques. Index Terms—Energy-efficient strategies, mobile sensors, opti- mization, target monitoring, wireless sensor networks. I. INTRODUCTION S ENSOR networks have been envisioned as a means for gathering, monitoring, processing, and delivering informa- tion about the physical environment to the intended recipient(s) [1], [2]. This area of research has attracted much attention in both control and communication literature in recent years [3]–[6]. A mobile sensor network (MSN) is typically com- prised of wireless mobile nodes equipped with battery-powered sensors. Such networks are known to be very effective in detecting, monitoring, and tracking dynamic targets and have important civilian and military applications [7]–[11]. Examples of such applications include robot-assisted sensor networks for data collection [12], security and surveillance [13]–[15], environmental monitoring [16]–[18], target tracking [19], [20], and structural health monitoring (SHM) [21], [22], to name only a few. In an MSN, each sensor communicates with a subset of sen- sors in the network and uses a proper movement strategy in order to achieve certain objectives such as covering a sensing Manuscript received May 31, 2011; accepted June 14, 2011. Manuscript received in final form August 29, 2011. Date of publication November 03, 2011; date of current version August 09, 2012. This work was supported by the National Institute of Standards and Technology (NIST) under Grant 70NANB8H8146. Recommended by Associate Editor M. Mesbahi. H. Mahboubi and A. G. Aghdam are with the Department of Electrical and Computer Engineering, Concordia University, Montréal, QC H3G 1M8, Canada (e-mail: h_mahbo@ece.concordia.ca; aghdam@ece.concordia.ca). A. Momeni is with the Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada (e-mail: amomeni@unb.ca). K. Sayrafian-Pour and V. Marbukh are with the National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899 USA (e-mail: ksayrafian@nist.gov; Marbukh@nist.gov). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TCST.2011.2167151 field, monitoring, or tracking a moving target with a trajectory which is not known a priori. The information exchange between the sensors and a proper algorithm to use the collected informa- tion in order to effectively relocate the mobile sensors are the two important components of any MSN control scheme. These components along with the capabilities of the individual sensors (in terms of battery power, communication range, and displace- ment flexibility) determine the efficacy of the MSN in achieving any desired objective [23], [24]. Recent developments in MEMS technology have provided a wealth of cheap, customizable, and embedded ad hoc wireless sensor systems [25]–[27]. There has been a burst of research ac- tivities in cross-layer network optimization in recent years, in- volving routing, flow and power control, and packet scheduling [28], [29]. The mathematical framework for such an optimiza- tion problem is based on the concept of elastic users and the cor- responding aggregate utility maximization; for instance, see the framework given in [30] in the context of network management. Price-based distributed algorithms concerning utility maximiza- tion for a wire-line network were developed in [31]. These algo- rithms assume that elastic users respond to congestion pricing signals by modifying their bandwidth requirements. More re- cent papers such as [32] and [33], extended the price-based al- gorithms to a wireless environment. Note that wireless networks have numerous advantages in sensor applications, due mainly to the distributed nature of this type of system. Despite recent successes in developing efficient utility max- imization algorithms, numerous issues remain open in sensor network control. For instance, providing different quality-of- service levels, effective decentralization of optimal power con- trol and packet scheduling, and, most important of all, devel- oping simple distributed algorithms to achieve robust perfor- mance in partially known environments are some of the areas that require further research. Different objective functions are introduced in the literature to evaluate the performance of the network. In this paper, a routing strategy is presented for the relocation of mobile sensors in a network and the adjustment of their com- munication and sensing range, such that a certain cost function is minimized, while the end-to-end connectivity from a moving target to a fixed access point (also called the destination point) is maintained. Various cost functions concerning individual sen- sors and the entire network will be considered to evaluate the performance of the network in terms of power consumption. A technique is also provided to maximize the durability of the whole network by monitoring the residual energy of individual sensors and adjusting their parameters accordingly. Simulation 1063-6536/$26.00 © 2011 IEEE