Efficient Solutions Framework for Optimal Multitask Resource Assignments for Data Fusion in Wireless Sensor Networks

Abstract

Motivated by the need to judiciously allocate scarce sensing resources to attain the highest benefit for the applications that sensor networks serve, in this article we develop a flexible solutions methodology for maximizing the overall reward attained, subject to constraints on the resource demands under fairly general reward or demand functions. We map a broad class of related problems for data fusion in wireless sensor networks into an integer programming problem and provide an iterative Lagrangian relaxation technique to solve it. Each iteration step involves solving for a maximum-weight independent set of an appropriately constructed graph, which, in many cases, can be obtained in polynomial time. We apply our methodology to the problem of tracking targets moving over a period of time through a nonhomogeneous, energy-constrained sensor field. With rewards represented by the quality of information attained in tracking, we study its tradeoffs and relationship with energy consumption and periodic measurement taking. We finally illustrate other applications of our framework in sensor networks. ACM Reference Format:Srikanth Hariharan, Chatschik Bisdikian, Lance M. Kaplan, and Tien Pham. 2014. Efficient solutions framework for optimal multitask resource assignments for data fusion in wireless sensor networks. ACM

ADAI and Adaptive PSO-Based Resource Allocation for Wireless Sensor Networks

“…For WSNs in static environments, Integer Linear Programming (ILP) is applied to the task assignment problem of the network in different scenarios [8], which considers the communication and computational overhead of the node. The literature [9] also applies the ILP method for resource allocation in the network, but the information quality is the primary consideration in the allocation process. However, due to the growing user demand, the network requires an efficient and dynamic resource allocation mechanism.…”

A Novel Two-Tier Cooperative Caching Mechanism for the Optimization of Multi-Attribute Periodic Queries in Wireless Sensor Networks

“…Traditional techniques have studied the query processing in WSNs from various aspects, including in-network query processing [ 18 ], aggregated query processing [ 19 ], compressed data aggregation [ 20 ], spatial correlation data aggregation [ 21 ], range query processing [ 22 ], opportunistic sampling-based query processing [ 23 ], snapshot and continuous data aggregation [ 17 , 24 ], real-time query processing [ 25 ], multiple dimensional or attributes query optimization [ 26 – 28 ], cooperative caching-based query processing [ 29 , 30 ], etc. Generally, these techniques are mostly exploring the one-shot query scheduling, where one single attribute is interested, whereas few efforts study periodic, aggregated and multi-attribute query processing [ 31 ].…”