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In recent years, a disruption-tolerant network (DTN) has attracted significant attention regarding lunar exploration, as they can be employed in various relay spacecraft, such as lunar orbiters, due to their excellent constant all-time coverage capability. An exclusive relay satellite with limited storage capacity and downlink communication bandwidth to Earth could be congested with huge amounts of return data from multiple sources on the lunar surface, resulting in low efficiency in the backhaul link. To address this issue, a network-coded forwarding scheme employing a Markov-decision-assisted bundle aggregation for a Halo-orbit Earth–lunar DTN is proposed in this article. Specifically, a network-coding-enabled store-and-forward scheme is designed for the downlink relay delivery in the L2-point Halo orbit using a two-priority bundle-aggregation mechanism. A size-optimal bundle slicing-aggregation algorithm is developed for the two-source network coding scheme using the Markov decision algorithm. Simulation results demonstrate that the proposed mechanism exhibits better performance than existing delivery schemes in reducing end-to-end delivery latency. Thus, the throughput can be improved, and the arrival rate of high-priority bundles can be guaranteed. [ABSTRACT FROM AUTHOR]

Industrial Internet of things (IIoT) is expected to improve efficiency and productivity by connecting massive devices, but it will cause potential congestions in backhual link and high energy consumptions. Caching with coded transmission is an effective method to reduce backhual load for content delivery. However, due to the hierarchy and heterogeneity in IIoT, it is very challenging to perform content placement with lower energy consumption. In this article, we propose an energy-efficient content placement strategy in cache-enabled hierarchical IIoT network with coded transmission. We derive a closed-form expression including the energy consumption for content placement and transmission by the macro base station and the small base stations. In addition, we establish an optimization problem to minimize the total energy consumption, whereby we find the optimal content placement matrix and optimal cache size allocation, respectively. Simulation results show that, the proposed content placement strategy can greatly improve energy efficiency in IIoT. [ABSTRACT FROM AUTHOR]

In modern transportation systems, the road intersections where multiple roads merge usually become the traffic bottleneck. Existing coordination solutions such as the traffic signs tend to be ineffective. In order to exploit the full potential of the road infrastructure, we try to address the optimal vehicle coordination problem, by which the traffic throughput can be maximized. We first formulate it as a general centralized collision-free traffic scheduling framework. The involved vehicles could cooperate via instructions from the coordination center, by solving the essential non-convex coordination problem. Aiming at the dynamic arrival of vehicles, a corresponding coordination strategy is thus proposed to achieve better quality of service (QoS), including the traffic throughput, and delay. The queue stability on each road can be guaranteed according to the Rate Stability Theorem, and Lyapunov Theorem. We then try to extend the proposed intelligent coordination framework to the multi-collision-set one, by which the traffic throughput can be significantly improved. A spatial domain based solution is given to solve the multi-collision-set coordination problem. Due to the essential non-convex formulation, high accuracy linearization approximate algorithms are also provided. In the numerical results, we show the vehicle maneuvers, and statistical coordination time as the performance evaluation. They validate our analysis, and show the performance advantages achieved by the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel multi-slot pilot allocation (MSPA) random access scheme with unequal access latency (UAL) protection for user equipments (UEs) in massive machine-type communications (mMTC). In order to provide UAL protection, we allocate the UEs into different groups according to their UAL requirements, where the higher priority groups can access in a stringent latency under the required access failure probability (AFP) requirement; while the lower priority groups are able to access with predetermined AFP by allocated multi-slot. Specifically, our generalized UAL-MSPA random access protocol is incorporated into two considered UAL protection schemes, i.e., the independent UAL scheme and the expanded UAL scheme. We derive the closed-form expressions to the AFP and the sum throughout for the investigated UAL schemes. By utilizing these analytical results, a joint parameter optimization problem is formulated for obtaining the minimum access latency under the desired AFP requirements. Detailed simulations validate our analytical results and confirm the efficiency of our proposed UAL schemes. Finally, the UAL-MSPA random access protocol with joint optimized parameters outperforms the existing protocols in terms of achieving high sum throughput and shortening the access latency of low priority group. [ABSTRACT FROM AUTHOR]

High-precision location information formulates the basis of many modern wireless location-based services (LBSs). However, location privacy becomes an important issue along with the advantages that LBS offer. In this paper, we first show that a novel eavesdropper is able to perform position estimation of the agent by purely overhearing the measurement signals between anchors and the agent, using a time difference of arrival way. Optimal power allocation-based approaches are presented to protect the location privacy against the eavesdropper. Then, high-accuracy approximation algorithms are also presented to solve the essential non-convex optimization problems. According to the numeric results, we can see that the presented power allocation strategies are able to prevent the eavesdropper from performing accurate position estimation of the agent. We can also see that there exists an optimal tradeoff between the system localization and privacy protection, which can be achieved by the presented frameworks. [ABSTRACT FROM AUTHOR]

Cooperative localization can enhance the accuracy of wireless network localization by incorporating range information among agent nodes in addition to those between agents and anchors. In this paper, we investigate the optimal allocation of the restricted resources, namely, power and bandwidth, to different nodes. We formulate the optimization problems for both synchronous networks and asynchronous networks, where one way and round trip measurements are applied for range estimation, respectively. Since the optimization problems are nonconvex, we develop an iterative linearization-based technique, and show by comparison with brute-force search that it provides near-optimal performance in the investigated cases. We also show that especially in the case of inefficient anchor placement and/or severe shadowing, cooperation among agents is important and more resources should be allocated to the agents correspondingly. [ABSTRACT FROM PUBLISHER]

Network localization is a key feature in many wireless services and applications. In typical range-based non-cooperative localization techniques, agents try to perform position estimation through ranging with respect to anchors with known positions. Based on the definition of squared positional error bound, the localization accuracy can be determined by the transmit power, carrier frequency, and signal bandwidth. This paper analyzes the joint power and spectrum allocation (JPSA) optimization problems in resource restricted wireless localization systems. We first formulate both optimal interference-free and interference affected JPSA problems. We then formulate the robust counterparts of the problems in the presence of uncertainty of the agents’ positions. Since all JPSA problems are non-convex, we show that they can be modeled and solved as geometric programming (GP) by proper approximations. Numeric results validate our analysis and show that the developed algorithms are able to find solutions close to the global optimum in the investigated cases. We can find the optimal/robust resource deployment in non-cooperative wireless localization networks based on the proposed frameworks. [ABSTRACT FROM PUBLISHER]

In this letter, we consider the scheduling and resource allocation problems in asynchronous ultra wideband cooperative localization networks, where the round trip measurements are applied for range estimation. A general non-data-aided cooperative scheduling strategy is first presented, where the reply signals can be identified by different frequency bands. Therefore, extra information, such as node IDs and processing times, is not required in the reply signals. We then perform resource optimization based on the scheduling strategy using a high accuracy approximate algorithm. Numeric results validate the analysis. We also find that the importance of cooperative localization increases if the anchors are badly deployed or suffer from significant blockages with agents. [ABSTRACT FROM PUBLISHER]

This letter investigates the selection of false alarm (FA) probability for dynamic spectrum access (DSA). We suggest that the performance of DSA systems can be improved efficiently by properly selecting the value of FA probability. The problem of DSA process with imperfect sensing is modeled as a continuous-time Markov chain and based on which, an optimization problem is formulated to maximize the number of served secondary radios for long-run term. We also present a simple gradient-based algorithm to find the optimal FA probability. Numerical results show that the proposal has better performance than conventional constant false alarm ratio based scheme. [ABSTRACT FROM PUBLISHER]