Abstract
With the increasing application of Internet of Things (IoT), the localization of IoT devices has been widely used. The distributed cooperative localization is expected to be applied in a large-scale dynamic network, such as IoT. It is located through the exchange of information among multiple nodes. For a large amount of battery-based users, the high-computational complexity and heavy communication overhead will lead to huge energy consumption. In this paper, we propose a link selection algorithm based on the evolutionary overlapping coalitional (EOC) game to mitigate the energy consumption for distributed cooperative localization in the dynamic network. The equivalent Fisher information matrix (EFIM) and the Cramér–Rao lower bound (CRLB) are employed to keep location accuracy. Numerical results verify that the distributed cooperative localization based on the EOC game achieves lower energy consumption while keeping localization accuracy in the dynamic networks.
Highlights
Internet of ings (IoT) is fast increasing key scenes in wireless network [1, 2]
We propose an evolutionary overlapping coalitional (EOC) game for distributed cooperative localization in the large-scale dynamic network, to decrease energy consumption while keeping localization accuracy
We design an EOC game in the dynamic location network. en, we propose a link selection algorithm to solve the limitation of distributed cooperative localization and employ the location algorithm of posterior linearization belief propagation (PLBP) to verify the effectiveness and predominance of our proposed algorithm in static and dynamic networks
Summary
Internet of ings (IoT) is fast increasing key scenes in wireless network [1, 2]. Localization technology plays an important role in Internet of ings (IoT), intelligent transportation system, and indoor environment [3,4,5]. For the nodes of IoT, due to energy limitation, it is necessary to design an efficient link selection mechanism in distributed cooperative localization. In [11], the link selection algorithm based on coalitional game is exploited to achieve a tradeoff between localization accuracy and network consumption which includes communication costs and computing resource consumption. The abovementioned link selection algorithms are based on the static game and do not consider the dynamic network topology. We propose an evolutionary overlapping coalitional (EOC) game for distributed cooperative localization in the large-scale dynamic network, to decrease energy consumption while keeping localization accuracy. En, we propose a link selection algorithm to solve the limitation of distributed cooperative localization and employ the location algorithm of posterior linearization belief propagation (PLBP) to verify the effectiveness and predominance of our proposed algorithm in static and dynamic networks We design an EOC game in the dynamic location network. en, we propose a link selection algorithm to solve the limitation of distributed cooperative localization and employ the location algorithm of posterior linearization belief propagation (PLBP) to verify the effectiveness and predominance of our proposed algorithm in static and dynamic networks
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