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Using Misbehavior to Analyze Strategic versus Aggregate Energy Minimization in Wireless Sensor Networks

机译:使用不当行为来分析无线传感器网络中的战略性能源和总能源最小化

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We present a novel formulation of the problem of energy misbehavior and develop an analytical framework for quantifying its impact on other nodes. Specifically, we formulate two versions of the power control problem for wireless sensor networks with latency constraints arising from duty cycle allocations. In the first version, strategic power optimization, nodes are modeled as rational agents in a power game, who strategically adjust their powers to minimize their own energy. In the other version, joint power optimization, sensor nodes adjust their transmission powers to minimize the aggregate energy expenditure. Our analysis of these models yields insight into the different energy outcomes of strategic versus joint power optimization. We show that while joint power optimization fits the accepted paradigm of cooperation among sensor nodes (for example large number of sensor nodes cooperating for a task such as target tracking), it comes with both advantages and disadvantages when energy misbehavior is taken into account. One advantage is that it can (sometimes) be energy-dominant, i.e., the optimal energy cost for each node under joint energy minimization is lower than its strategically optimal energy cost. We then develop a model for characterizing energy misbehavior and show that joint optimization is disadvantageous because it is impossible to prevent misbehavior under any channel quality and load constraints, whereas strategic optimization is more resilient. We prove that it is impossible for anode to unilaterally and undetectably follow a different energy optimization strategy than the other nodes and hence the only threat to the network is misbehavior through false advertisement. We then provide sufficient conditions under which misbehavior through false advertisement can be prevented under a strategic optimization regime. Our analytical results reveal optimal strategies for attacking nodes in an enemy network through energy depletion and help develop effective defense mechanisms for protecting our own wireless network against energy attacks by an intelligent adversary.
机译:我们提出了一种能源不当行为问题的新颖表述,并建立了一个量化其对其他节点影响的分析框架。具体来说,我们为无线传感器网络制定了两种版本的功率控制问题,它们具有因占空比分配而引起的延迟约束。在第一个版本中,战略性权力优化是将节点建模为权力游戏中的理性主体,他们会战略性地调整其权力以最大程度地减少自身的能量。在另一个版本中,联合功率优化,传感器节点调整其传输功率,以最大程度地减少总能量消耗。通过对这些模型的分析,可以深入了解战略与联合动力优化的不同能源成果。我们表明,虽然联合功率优化适合传感器节点之间的协作(例如,大量传感器节点协作完成目标跟踪等任务)的公认范式,但考虑到能量不当行为时,它既有优点也有缺点。一个优点是它可以(有时)是能量主导的,即在联合能量最小化的情况下,每个节点的最佳能量成本低于其战略上最佳的能量成本。然后,我们开发了一个表征能量不当行为的模型,并表明联合优化是不利的,因为在任何信道质量和负载约束下都不可能防止不当行为,而战略优化则更具弹性。我们证明,阳极不可能单方面且不可检测地遵循与其他节点不同的能源优化策略,因此,对网络的唯一威胁是由于虚假广告而产生的不良行为。然后,我们提供了充分的条件,在这种条件下,可以在战略优化机制下防止通过虚假广告引起的不良行为。我们的分析结果揭示了通过能量消耗来攻击敌方网络中节点的最佳策略,并有助于开发有效的防御机制来保护我们自己的无线网络免受智能对手的能量攻击。

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