This paper considers the centralized spectrum allocations in resource-constrained wireless sensor networks with the following goals: (1) allocate spectrum as fairly as possible, (2) utilize spectrum resource maximally, (3) reflect the priority among sensor data, and (4) reduce spectrum handoff. The problem is formulated into a multi-objective problem, where we propose a new approach to solve it using modified game theory (MGT). In addition, cooperative game theory is adopted to obtain approximated solutions for MGT in reasonable time. The results obtained from numerical experiments show that the proposed algorithm allocates spectrum bands fairly with well observing each sensor's priority and nearly minimal spectrum handoffs.

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During the last decade, a plentiful number of active queue management schemes have been proposed, but their main objectives are simply allocating the buffer resource to all flows evenly, or protecting responsive flows from being degraded by unresponsive flows. However, the sending rates of the responsive flows can be determined diversely, and not all unresponsive flows have aggressively high sending rates. Furthermore, it is rational to reserve a portion of the buffer resource for certain privileged traffic. Grounded by these evidences, in this paper, we present a resilient active queue management algorithm, named Prior-Core-based Buffer Allocation considering diverse congestion control algorithms, fair-unresponsive flows, and some privileged traffic. Our approach is based on stochastic cooperative game theory, where the payoffs yielded by cooperation are described by random variables, and the core is defined only over the distribution of these random payoffs; the core in this situation is called the prior-core. As a result, it is shown that our buffer allocation, yielded by the prior-core, achieves completely fair allocation for those flows whose requirement does not exceed the fair-share regardless of the responsiveness, whereas aggressive flows are restricted according to availability of the buffer; all these are verified through ns-2 simulation experiments. Every one second, we count the number of packets arrived in the buffer as a sample for the ith flow and calculate its weighted moving average: Sampli ng t D w Sampli ng t 1 C .1 w/ .a sample/, where w is 0.8.