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Coordinating multiple agents with diverse tasks and changing goals without interference is a challenge. Multiagent reinforcement learning (MARL) aims to develop effective communication and joint policies using group learning. Some of the previous approaches required each agent to maintain a set of networks independently, resulting in no consideration of interactions. Joint communication work causes agents receiving information unrelated to their own tasks. Currently, agents with different task divisions are often grouped by action tendency, but this can lead to poor dynamic grouping. This article presents a two-phase solution for multiple agents, addressing these issues. The first phase develops heterogeneous agent communication joint policies using a group communication MARL framework (GCMA). The framework employs a periodic grouping strategy, reducing exploration and communication redundancy by dynamically assigning agent group hidden features through hypernetwork and graph communication. The scheme efficiently utilizes resources for adapting to multiple similar tasks. In the second phase, each agent's policy network is distilled into a generalized simple network, adapting to similar tasks with varying quantities and sizes. GCMA is tested in complex environments, such as StarCraft II and unmanned aerial vehicle (UAV) take-off, showing its well-performing for large-scale, coordinated tasks. It shows GCMA's effectiveness for solid generalization in multitask tests with simulated pedestrians.

Even though the traffic systems in developed countries have been analyzed rigorously and operated efficiently, the same does not generally hold for developing countries due to inadequate planning, design, and operations of their transportation systems. Because of inherent differences between internal infrastructures, the strategies deployed in developed countries may not be amenable to developing ones. Besides, developing countries’ traffic systems are not well-studied in the literature to the best of our knowledge. For example, it is yet to explore how a developed country’s lane-based traffic flow would perform in the context of a developing country, which generally experiences non-lane-based traffic. As such, by using our newly developed traffic simulator ‘RoadBird,’ we investigate outcomes of both lane-based and non-lane-based traffic from the contexts of both developing and developed countries. To do so, we run simulations over real road topologies (extracted from the GIS maps of major cities such as Dhaka, Miami, and Riyadh), considering different scenarios such as lane-based or non-lane-based flows, homogeneous or heterogeneous traffic, with or without pedestrians, etc. We also incorporate various car-following and lane-changing models to mimic traffic behaviors and investigate their performances. While the lane changing dilemma remains an open research question, our experimental evidence indicates: 1) lane-based approaches will not necessarily perform better in the case of currently-adopted non-lane-based scenarios, and 2) non-lane-based strategies may benefit system performance in lane-based scenarios while having heavy mixed traffic. Nonetheless, we reveal several new insights for on-road experiences both in developing and developed countries.

With the high demand for using location-based services (LBSs) in our daily lives, the privacy protection of users' trajectories has become a major concern. When users utilise LBSs, their location and trajectory information may expose their identities in continuous LBSs. Using the spatial and temporal correspondences on users' trajectories, adversaries can easily gather their private information. Using collaboration between users instead of location service providers (LSPs) reduces the chance of revealing private information to adversaries. However, there is an assumption of a trusting relationship between peers. In this paper, we propose a trustworthy collaborative trajectory privacy (TCTP) scheme, which anonymises users' trajectories and resolves the untrustworthy relationship between users based on peer-to-region LBSs. Moreover, the TCTP scheme provides query content preservation based on a fake query concept in which we conceal the user's actual query among a set of queries. The results of several experiments with different conditions confirm that our proposed scheme can protect users' trajectory privacy successfully in a trustworthy and efficient manner.

Computing connected dominating sets (CDSs) have been widely used to construct virtual backbones in wireless sensor networks, in order to control topology, facilitate routing and extend network lifetime. Constructing a minimum CDS is an NP-hard problem. For any CDS algorithm, the size of the constructed CDS is usually considered to be the most important performance factor. In this paper, we propose three novel algorithms to construct CDS in wireless sensor networks. The algorithms are intended to generate a small CDS. The first algorithm has a performance factor of 5 from the optimal solution. This approximation outperforms the existing state-of-the-art approaches. The other two algorithms are variations of the first algorithm with performance improvements. We include the performance analysis and simulation results that show the effectiveness of our algorithms in reducing the CDS size.