Jalal Habibi

Distributed Deployment Strategies for Improved Coverage in a Network of Mobile Sensors With Prioritized Sensing Field

Efficient deployment strategies are proposed for a mobile sensor network, where the coverage priority of different points in the field is specified by a given function. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is utilized to find the coverage holes of the network for the case where the sensing ranges of different sensors are not the same. Under the proposed strategies, each sensor detects coverage holes within its MW-Voronoi region, and then moves in a proper direction to reduce their size. Since the coverage priority of the field is not uniform, the target location of each sensor is determined based on the weights of the vertices or the points inside the corresponding MW-Voronoi region. Simulations validate the theoretical results.

A nonlinear optimization approach to coverage problem in mobile sensor networks

A distributed Voronoi-based sensor deployment approach is proposed to optimize sensor network coverage. It is assumed that each sensor can construct its Voronoi polygon using the information it receives from the neighboring sensors. To increase the local coverage of the sensors, it is desired to find a point in each polygon, such that if the corresponding sensor is placed there, then its covered area within the polygon is maximized. A nonlinear optimization algorithm is proposed based on the gradient projection to find the optimal location for each sensor. The algorithm can be implemented in a distributed fashion with minimum communication among the sensors. Examples are provided to demonstrate the effectiveness of the proposed approach in terms of convergence rate, coverage performance, and energy consumption.

A Gradient-Based Coverage Optimization Strategy for Mobile Sensor Networks

A Voronoi-based strategy is proposed to maximize the sensing coverage in a mobile sensor network. Each sensor is moved to a point inside its Voronoi cell using a coverage improvement scheme. To this end, a gradient-based nonlinear optimization approach is utilized to find a target point for each sensor such that the local coverage increases as much as possible, if the sensor moves to this point. The algorithm is implemented in a distributed fashion using local information exchange among sensors. Analytical results are first developed for the single sensor case, and are subsequently extended to a network of mobile sensors, where it is desirable to maximize network-wide coverage with fast convergence. It is shown that under some mild conditions, the positions of the sensors converge to a stationary point of the objective function, which is the overall weighted coverage of the sensors. Simulations demonstrate the effectiveness of the proposed strategy.

Optimal Cooperative Wireless Transmission With Limited Channel State Information

This paper tackles the problem of minimum-energy cooperative transmission in wireless networks under the assumption of limited channel state information at the transmitters. It is assumed that only the average statistics of the fading channels are known to the transmitters. The objective here is to jointly optimize the set of relays and the transmission powers for both the broadcasting and cooperative transmission phases while satisfying probabilistic signal-to-noise ratio (SNR) constraints at the relays and at the destination node. Increasing the broadcasting power expands the set of potential relays and decreases the required power for cooperative transmission. Hence, there is a compromise in the selection of the power values, which is addressed in this work using a chance-constrained optimization framework. A closed-form approximate solution is also presented, which provides a low-complexity transmission scheme for energy-harvesting wireless networks. Simulations are presented to demonstrate the efficacy of the proposed approach and the approximate solution.

A Framework for Evaluating the Best Achievable Performance by Distributed Lifetime-Efficient Routing Schemes in Wireless Sensor Networks

This paper is concerned with energy-efficient routing in wireless sensor networks. Most of the existing routing schemes assign energy-related costs to network links and obtain the shortest paths for the nodes to balance the flowing traffic within the network and increase its lifetime. However, the optimal link cost values and the maximum achievable lifetime are not known for the majority of the existing schemes. A framework is provided in this work to analytically derive the best achievable performance that can be obtained by any distributed routing algorithm based on the shortest-path approach. Given a network configuration and an energy consumption model, the presented framework provides the optimal link cost assignment which yields the maximum lifetime in a distributed shortest-path routing strategy. The results are extended to the case of variable link cost assignment as well. A heuristic algorithm is also developed to obtain approximate solutions to the best performance problem with limited computational complexity. In particular, the proposed framework provides the optimal route selection as a benchmark to evaluate the energy efficiency of existing routing algorithms.

Generalized formulation for trajectory optimization in patrolling problems

In this paper, a general formulation is presented for trajectory optimization in patrolling problems. It is assumed that the starting depots are not prespecified. This assumption distinguishes the present work from the existing literature. A number of viewpoints are assigned to be visited in a certain sequence to minimize the total travel distance. The problem turns out to be a variant of the well-known Traveling Salesmen Problem (TSP), namely the Multidepot multiple Traveling Salesmen Problem (MmTSP). A comparison between the commonly-used pre-specified starting multidepot approach and the proposed formulation is performed and the efficacy of the results is presented through simulations.

Laser cavity squeezing using optimal servo controller in optomechanical sensors

In this paper, the linear quadratic Gaussian (LQG) control method is used to tackle the problem of mode-squeezing in optomechanical sensors. Coherent-feedback quantum control is synthesized by incorporating both shot noise and back-action noise to attenuate the output noise well below the shot noise level. The effectiveness of the proposed control strategy in squeezing the cavity output beam is demonstrated by simulation.

Cooperative Self-Deployment Strategies in a Mobile Sensor Network with Non-Uniform Coverage Priority

Efficient deployment strategies are proposed for a mobile sensor network, where the coverage priority of different points in the field is specified by a prescribed weight function. According to these strategies, each sensor detects coverage holes within its Voronoi polygon, and then moves in a proper direction to reduce them. Since the coverage priority of different points in the field is not the same, the target location of each sensor is determined based on the weights of the points inside the corresponding Voronoi polygon (or its vertices). Simulations confirm the theoretical results.

Energy-efficient cooperative transmission in wireless networks with limited channel state information

This paper tackles the problem of minimum-energy cooperative transmission in wireless networks under the assumption of limited channel state information at the transmitters. It is assumed that only the average statistics of the fading channels are known to the transmitters. The objective here is to jointly optimize the set of relays and the transmission powers for both the broadcasting and cooperative transmission phases while satisfying probabilistic signal-to-noise ratio (SNR) constraints at the relays and at the destination node. Increasing the broadcasting power expands the set of potential relays and decreases the required power for cooperative transmission. Hence, there is a trade-off in the selection of the power values, which is addressed in this work by using a chance-constrained optimization framework. Simulations are presented to demonstrate the efficacy of the proposed approach.

A framework for analyzing the performance of distributed lifetime-efficient routing schemes in wireless sensor networks

This paper is concerned with energy-efficient routing in wireless sensor networks. Most of the existing routing schemes assign energy-related costs to network links and obtain shortest paths for the nodes in order to balance the flowing traffic within the network and increase the lifetime. However, the optimal link cost values and the maximum achievable lifetime are not known for this vast category of schemes. A framework is provided in this work to analytically derive the best achievable performance by any distributed routing algorithm based on the shortest-path approach. Given a network configuration and an energy consumption model, the presented framework provides the optimal link cost assignment which yields the maximum lifetime in a distributed shortest-path routing strategy. The results are extended to the case of variable link cost assignment as well. In particular, the proposed framework provides the optimal route selection as a benchmark to evaluate the energy efficiency of existing routing algorithms.