Unmanned aerial vehicles (UAVs) have attracted plenty of attention due to their high flexibility and enhanced communication ability. However, the limited coverage and energy of UAVs make it difficult to provide timely wireless service for large-scale sensor networks, which also exist in multiple UAVs. To this end, the advanced collaboration mechanism of UAVs urgently needs to be designed. In this paper, we propose a multi-UAV collaborative scheme for seamless data collection and transmission, where UA s are dispatched to collection points (CPs) to collect and transmit the time-critical data to the ground base station (BS) simultaneously through the cooperative backhaul link. Specifically, the mission completion time is minimized by optimizing the trajectories, task allocation, collection time scheduling, and transmission topology of UAVs while ensuring backhaul link to the BS. However, the formulated problem is non-convex and challenging to solve directly. To tackle this problem, the CP locations and transmission topology of UAVs are obtained by sensor node (SN) clustering and region division. Next, the transmission connectivity condition between UAVs is derived to facilitate the trajectory discretization and thus reduce the dimensions of variables. This simplifies the problem to optimizing the UAV hovering locations, hovering time, and CP serving sequence. Then, we propose a point-matching-based trajectory planning algorithm to solve the problem efficiently. The simulation results show that the proposed scheme achieves significant performance gains over the two benchmarks.

Low-latency and high-precision vehicle localization plays a significant role in enhancing traffic safety and improving traffic management for intelligent transportation. However, in complex road environments, the low latency and high precision requirements could not always be fulfilled due to the high complexity of localization computation. To tackle this issue, we propose a road-aware localization mechanism in heterogeneous networks (HetNet) of the mobile communication system, which enables real-time acquisition of vehicular position information, including the vehicular current road, segment within the road, and coordinates. By employing this multi-scale localization approach, the computational complexity can be greatly reduced while ensuring accurate positioning. Specifically, to reduce positioning search complexity and ensure positioning precision, roads are partitioned into low-dimensional segments with unequal lengths by the proposed singular point (SP) segmentation method. To reduce feature-matching complexity, distinctive salient features (SFs) are extracted sparsely representing roads and segments, which can eliminate redundant features while maximizing the feature information gain. The Cramér-Rao Lower Bound (CRLB) of vehicle positioning errors is derived to verify the positioning accuracy improvement brought from the segment partition and SF extraction. Additionally, through SF matching by integrating the inclusion and adjacency position relationships, a multi-scale vehicle localization (MSVL) algorithm is proposed to identify vehicular road signal patterns and determine the real-time segment and coordinates. Simulation results show that the proposed multi-scale localization mechanism can achieve lower latency and high precision compared to the benchmark schemes.

With the rapid expansion of autonomous vehicles and entertainment applications, video traffic in the Internet of Vehicles (IoV) faces exponential growth. This surge in video demand presents significant challenges for effective pre-caching strategies, particularly due to high vehicle mobility and heterogeneous dwell times at edge nodes caused by varying speeds. In this paper, we propose an efficient adaptive video segment pre-caching scheme (AVSC) for the IoV, addressing varying travel durations of vehicles on road. Specifically, we develop two video evaluation models to balance the popularity of cached video segments with the fidelity of their distribution across the entire video, ensuring temporal continuity. Then, a multi-objective optimization problem is formulated to jointly maximize highlight entropy and segment distribution fidelity. By leveraging the time-frequency characteristics of the wavelet transform, initial segment candidates are identified by detecting significant changes in the time series of chunk popularity (derived from analyzing frame-level popularity). This approach reduces the search space and computation time for subsequent segment selection. Based on the initial segment candidates, the highlight-direction optimal algorithm is proposed to iteratively identify highlight candidates by improving highlight entropy. For Pareto-optimal solutions, a caching-segment adjustment algorithm based on neighborhood search is proposed to determine the final cached video segments. Theoretical guarantees are provided for the identification process. Furthermore, the adjustment algorithm is proven to detect the maximal improvement direction of distribution fidelity, enhancing convergence speed. Simulations on real-world video datasets demonstrate the effectiveness of the proposed AVSC.

Unmanned aerial vehicles (UAVs) have received plenty of attention due to their high flexibility and enhanced communication ability, nonetheless, the limited onboard energy restricts UAVs’ application on persistent data collection missions in large areas. In this paper, we propose a rechargeable UAV-assisted periodic data collection scheme, where a UAV is dispatched to periodically collect data from sensor nodes (SNs) in the mission area and charged by a wireless charging platform. Specifically, the periodic data collection completion time is minimized by optimizing the UAV trajectory to reach the optimal balance among the collection time, flight time, and recharging time. The formulated problem is non-convex and difficult to solve directly. To tackle this problem, we divide the main problem into two sub-problems and address them by leveraging successive convex approximation (SCA), bisection search, and heuristic methods. Then, we propose a periodic trajectory optimization algorithm to iteratively solve the two sub-problems to minimize the completion time. Furthermore, to deal with the dynamics of SNs, we propose a low-complexity trajectory adjustment strategy, where the trajectory can be maintained or adjusted locally at the SNs change, which significantly mitigates the computation cost of re-optimization. The simulation results show the superiority and robustness of the proposed scheme and the completion time is on average 39% and 33% lower than the two benchmarks, respectively.

Vehicle localization is essential for intelligent trans-portation. However, achieving low-latency vehicle localization without sacrificing precision is challenging. In this paper, we propose a road-aware localization mechanism in heterogeneous networks (HetNet), where distinct features of HetNet signals are extracted for two-spatial-scale position mapping, enabling low latency with high precision. Specifically, we propose a sequence segmentation method to extract the low-dimensional positioning space on two scales. To represent roads and sub-segments according to HetNet signals, we propose a salient feature ex-traction method to eliminate redundant features and retain distinct features, thereby reducing feature-matching complexity and improving representation accuracy. Based on the extracted salient features, a two-spatial-scale localization algorithm is designed through salient feature matching, which can achieve low-latency road-aware localization. Furthermore, high-precision positioning is achieved by coordinate mapping based on curve fitting. Simulation results show that our mechanism can provide a low-latency and high-precision positioning service compared to the benchmark schemes.

Autonomous driving and intelligent transportation applications have dramatically increased the demand for high-accuracy and low-latency localization services. While cellular networks are potentially capable of target detection and localization, achieving accurate and reliable positioning faces critical challenges. Particularly, the relatively small radar cross sections (RCS) of moving targets and the high complexity for measurement association give rise to weak echo signals and discrepancies in the measurements. To tackle this issue, we propose a novel approach for multi-target localization by leveraging the controllable signal reflection capabilities of intelligent reflecting surfaces (IRSs). Specifically, IRSs are strategically mounted on the targets (e.g., vehicles and robots), enabling effective association of multiple measurements and facilitating the localization process. We aim to minimize the maximum Cramér-Rao lower bound (CRLB) of targets by jointly optimizing the target association, the IRS phase shifts, and the dwell time. However, solving this CRLB optimization problem is non-trivial due to the non-convex objective function and closely coupled variables. For single-target localization, a simplified closed-form expression is presented for the case where base stations (BSs) can be deployed flexibly, and the optimal BS location is derived to provide a lower performance bound of the original problem. Then, we prove that the transformed problem is a monotonic optimization, which can be optimally solved by the Polyblock-based algorithm. Moreover, based on derived insights for the single-target case, we propose a heuristic algorithm to optimize the target association and time allocation for the multi-target case. Furthermore, we provide useful guidance for the practical implementation of the proposed localization scheme by theoretically analyzing the relationship between time slots, BSs, and targets. Simulation results verify that deploying IRS on vehicles and effective phase shift design can effectively improve the resolution ability of multi-vehicle positioning and reduce the requirements of the number of BSs.

Under numerous computation requirements in intelligent traffic, platoons comprised of several connected vehicles are expected to centralize vehicular computation resources, which can provide computing services for surrounding mobile users and thus facilitate the deployment of vehicular edge computing (VEC). Nevertheless, the computing resources in multi-platoon are distributed unevenly, making platoons’ resource allocation highly selective, especially for randomly distributed mobile users. Moreover, existing works mainly focus on single-platoon-assisted VEC, which lacks resource coordination among platoons and may result in resource imbalance. Thus, through coordinating computing resource allocation in platoons and base stations (BSs), a coordinated computing resource allocation scheme is proposed in this paper to maximize the utilities of mobile users. However, the formulated problem is a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. To address this issue, the efficiency-maximized optimal computing resources allocated from platoons to users are derived in a semi-closed form. Then, to decouple the variables in allocation coordination, the efficiency maximization problem is proven to be equivalent to a low-complexity resource allocation problem oriented for single users. Based on the above results, two algorithms are proposed to convert the NP-hard problem into convex ones, 1) through efficiency-maximized task offloading and backtracking iteratively, a profit efficiency backtracking algorithm is proposed to coordinate computing resource allocation among platoons, and 2) heterogeneous profit efficiency algorithm is proposed to solve the primal problem, where a partition ratio-based efficiency maximization computing resource allocation problem is optimized in heterogeneous edges. Extensive simulation results show that our proposed scheme can improve resource allocation efficiency, task success rates, and service continuity over benchmark schemes.

Vehicle-to-everything (V2X) communication is expected to support many promising applications in next-generation wireless networks. The recent development of integrated sensing and communications (ISAC) technology offers new opportunities to meet the stringent sensing and communication (S&C) requirements in V2X networks. However, considering the relatively small radar cross section (RCS) of the vehicles and the limited transmit power of the road site units (RSUs), the power of echoes may be too weak to achieve effective target detection and tracking. To handle this issue, we propose a novel sensing-assisted communication scheme by employing an intelligent omni-surface (IOS) on the surface of the vehicle. First, a two-phase ISAC protocol, including the S&C phase and the communication-only phase, was presented to maximize the throughput by jointly optimizing the IOS phase shifts and the sensing duration. Then, we derive a closed-form expression of the achievable rate which achieves a good approximation. Furthermore, a sufficient and necessary condition for the existence of the S&C phase is derived to provide useful insights for practical system design. Simulation results demonstrate the effectiveness of the proposed sensing-assisted communication scheme in achieving a high throughput with low transmit power requirements.

The advent of 5G opens up hitherto unimagined opportunities for delivering the much-anticipated Internet of Vehicles (IoV). There are two typical types of services in IoV, i.e., safety service and non-safety service. However, the existing IoV framework cannot efficiently support the differentiated IoV services. In addition, maximizing the number of accessed vehicles/users is another critical issue in IoV, especially in the dense urban area. To address these problems, in this paper, we utilize the emerging network slicing technology to support different services and employ Non-Orthogonal Multiple Access technology (NOMA) to help increase the connectivity. In particular, for the safety service, we take advantage of the finite blocklength capacity to correctly record the delay. Our goal is to maximize the connectivity of users by jointly considering the user association and their beamforming vectors, under the restrictions of limited physical resource. The problem is formulated as a Mixed-Integer Nonlinear Programming problem (MINLP). To tackle the intractable MINLP, we propose a two-stage scheme. Firstly, we exploit efficient approaches to solve the beamforming problem, i.e., successive convex approximation, semidefinite relaxation and second-order cone programming. Secondly, we propose a low-complexity Greedy User Association (GUA) algorithm to solve the user association problem. Finally, comprehensive simulations verify that our proposed GUA algorithm is close to global optimal solution and outperforms the benchmark schemes.

Due to the features of on-demand deployment and flexible observation, unmanned aerial vehicles (UAVs) are promising for serving as the next-generation aerial sensors by using their onboard sensing devices. Compared to a single UAV with limited sensing coverage and communication capability, multi-UAV cooperation is able to realize more effective sensing and transmission (S&T) services, and delivers the sensory data to the control center more efficiently for further analysis. Nevertheless, most existing works on multi-UAV sensing mainly focus on mutually exclusive task allocation and independent data transmission, which did not fully exploit the benefit of multi-UAV sensing and communication. Motivated by this, we propose a novel multi-UAV cooperative S&T scheme with overlapped sensing task allocation. Although overlapped task allocation may sound counter-intuitive, it can actually foster cooperative transmission among multiple UAVs through a virtual multi-antenna system and thus reduce the overall sensing mission completion time. To obtain the optimal task allocation and transmit power of the proposed scheme, a mission completion time minimization problem is formulated. To solve this problem, a condition that specifies whether it is necessary for the UAVs to perform overlapped sensing is derived. For the cases of overlapped sensing, this time minimization problem is transformed into a monotonic optimization and is solved by the generic Polyblock algorithm. To efficiently evaluate the mission completion time in each iteration of the Polyblock algorithm, new auxiliary variables are introduced to decouple the otherwise sophisticated joint optimization of transmission time and power. While for the degenerated case of non-overlapped sensing, the closed-form expression of the optimal transmission time is derived, which provides insights into the optimal solution and facilitates the design of an efficient double-loop binary search algorithm to optimally solve the degenerated problem. Finally, simulation results demonstrate that the proposed scheme significantly reduces the mission completion time over benchmark schemes.

Real-time search is essential for accessing specific sensing data (SD) in vehicular networks to support safe, efficient, and intelligent road services. Considering the tremendous data volume, the SD search process should be carefully devised to avoid excessive retrieval and transmission delay. To alleviate the communication and computational burden for sensing devices and the cloud server, roadside edges are adopted to cache the SD in advance. Given a short lifetime of SD, the caching scheme is required to be efficient in facilitating both the search process and uplink/downlink transmission, which is quite challenging due to the coupling of resource allocation decisions. To guarantee the search efficacy and enhance the caching resource utilization, we propose a real-time search-driven caching (RSC) paradigm to enable the cooperation among storage-constrained edges. A hierarchical indexing framework is first introduced for cached data, based on which we then devise a search utility model to quantify the expected data freshness and response delay. With the objective of maximizing the long-term search reward, the RSC problem is formulated by jointly considering the search requests and utility model. A deep-reinforcement-learning-based caching (DRLC) method is proposed to solve the problem. Specifically, an action transition module is introduced to lower the computational complexity via reducing the selection space of caching actions. Extensive simulations are carried out based on the real trace data in Creteil, France, and results show that the intelligent DRLC method can improve the real-time search performance significantly comparing to the benchmark methods.

Mobile crowdsensing (MCS) can be widely adopted for undertaking a variety of tasks for the smart city by recruiting distributed participants. With the rapid proliferation of MCS applications, tasks can be devised with different sensing scales. Hence, allocating the tasks to participants with variable spatiotemporal granularity would be urgent and challenging. To this end, we propose a behavior sequentializing and task mapping (BETA) framework, where MCS tasks can be flexibly mapped based on the moving behavior of a participant’s daily activities. A sequential behavior model is first proposed to describe the moving behavior of a participant’s daily activities. Specifically, the behavior of a daily activity is represented by the spatiotemporal trajectory distribution, and a sequential behavior graph is adopted to model the relationships among multiple activities. To extract the sequential behavior from historical trajectories, a projection-clustering (PC) algorithm and a behavior sequentializing approach are developed. Then, based on the extracted sequential behavior, a behavior-to-task mapping network is proposed to evaluate the fitness between tasks and a participant. Finally, extensive simulations are conducted to demonstrate the functionality and performance of BETA. The results show that our proposed method outperforms the state-of-the-art solutions in the mapping efficiency of tasks with different sensing scales.

Aiming at traffic efficiency improvement, vehicle platoon becomes an essential driving technique to control the synchronous driving of connected and automated vehicles. Since the mature automated driving technology and cooperative willingness cannot be ensured for all vehicles, a mixed traffic paradigm that incorporates both cooperative vehicles (CVs) and non-cooperative vehicles (NCVs) will be inevitable. Due to the traffic hindrance and intermittent connections caused by the randomly distributed NCVs, traffic efficiency would be limited in the mixed traffic. Therefore, a hindrance-aware platoon formation scheme is proposed for the mixed traffic in this paper, which aims at coordinating CVs to maximize overall traffic velocity while minimize collision risk and fuel consumption. Specifically, a vehicle connection model is first established to construct the hindrance-aware relationships among vehicles, based on which, the intermittent connections of CVs are formulated as a mixed integer nonlinear programming (MINLP) problem. To reduce the search space, a novel cross-vehicle matrix is designed to represent the hindrances that each CV faces, then a traffic-maneuver-tree construction algorithm is proposed to search for feasible and hindrance-aware platoon formation connections. A length-dynamic inverse influence zone is constructed, then two decision indicators of platoon leader and follower are decoupled by evaluating leadership qualifications of all CVs. Given the hindrance-aware connections and leadership qualifications, a hindrance-aware platoon formation algorithm is proposed to jointly optimize the speed, safety and energy efficiency of CVs. To verify the effectiveness of the platoon formation mechanism, extensive simulations are conducted, and the results reveal that the road utilization, traffic capacity improvement and safety assurance in mixed traffic can be enhanced.

Real-time sensing data access is essential for vehicular networks to support safe, efficient, and intelligent road services. Considering the tremendous data volume, the sensing big data search process should be carefully devised to avoid excessive retrieval delay. Edge caching can effectively alleviate the traffic burden and shorten the data downloading route, where the sensing data has to be uploaded to the edge in advance. Given a short life-time of sensing data, the caching scheme is required to be efficient in facilitating both the search process and uplink/downlink transmission, which is challenging due to the coupling of resource allocation decisions. In this paper, an edge-cloud cooperative caching scheme is proposed. Specifically, to enable real-time data search, we first introduce a hierarchical indexing framework for cached data, based on which we then devise a search utility model to quantify the expected data freshness and response delay. Aiming at maximizing the search utility, a Caching-assisted Real-time Search (CRS) problem is formulated. Due to its NP-hardness, we devise a greedy-based algorithm to solve the CRS problem. Simulation results demonstrate that the proposed cooperative caching scheme can significantly improve the data freshness and cache hit ratio comparing to the benchmark schemes.

Predicting mobile users' web-access behavior can have substantial impacts on resource allocation and cost reduction for wireless networks. Therefore, we propose a machine learning platform to forecast the web traffic and access time of mobile users. Based on the observation, the traffic patterns exhibit complex dependency on time, location, and popularity of webpages. Thus, a Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) is developed based on distinct engineered features to learn and predict users' web browsing activities. Then, to forecast the future access-time of mobile users, we propose a Self-exciting Memory Neural Network (SMNN). The access activities are modeled as self-exciting point processes, and intensities are adopted for prediction. Moreover, we extend the proposed predicting framework to cell towers. To cope with the diversity of traffic at the cell tower, we resort to clustering methods to group the similar users of each tower. Then, we develop an LSTM model for each cluster separately to predict the web domain traffic activities for the cell tower. Finally, we show that our proposed models outperform the baseline prediction models based on cellular networks dataset. We also show that for the cell tower access prediction task, the clustering method can significantly improve the prediction accuracy.

Unmanned aerial vehicles (UAVs), also known as drones, have already been widely implemented in wireless networks for promoting network performance and enabling new services. To efficiently explore the diversity introduced by the mobility of UAV, many efforts have been made in the design of the UAV trajectory under various wireless scenarios. However, the continuity of a UAV trajectory in both time and topology forces researchers to approximate the UAV trajectory by a discrete model, which always results in a sub-optimal solution. To tackle the difficulty and obtain the optimal trajectory, in this work we introduce an artificial potential field (APF) to reformulate the objective in trajectory design, with which the UAV trajectory problem can be completely equivalent to a mechanical problem. In such mechanical problem, the UAV trajectory is represented by an extremely soft and thin rope with variable density carrying UAV speed information, and the original objective of optimizing the system performance is transformed to minimizing the overall artificial potential energy on the rope. As a result, the rope in the optimal solution stays in a state of equilibrium and the UAV trajectory can be equivalently optimized by designing the shape of a rope under the APF via mechanical principles. We provide a case study to describe in detail the problem equivalence, i.e., taking a single-user network as an example in which the throughput between UAV and the user is considered as the objective performance. In particular, the optimal trajectory of a UAV is constructed based on mechanical principles, while the global optimality is also rigorously proved and further confirmed via simulations. Moreover, we also highlight that the novel strategy of constructing equivalent mechanical problem has the possibilities to be extended to various UAV trajectory problems under different scenarios with different performance optimization objectives.

Sensor data search is an imminent component for the booming Internet of Things (IoT). Current proposals would refer to either keyword or spatial location, then the matched sensor data should be manually selected by requesters. However, the selection is arduous as there are massive connected devices. To this end, we propose a combinatorial-oriented feedback (COF) mechanism to guarantee the reliability and accessibility of feedback results via enabling an intuitive exhibition of aggregated sensor data. Two critical issues are addressed in this article: 1) sensor data combination and 2) aggregated results ranking. To facilitate the combination process, we first propose two decisive factors for aggregated result evaluation. The multiple sensor-data combinatorial (MSC) problem is converted into a multiobjective optimization model. To balance the tradeoff between competing quality metrics, we introduce Pareto sensor set (PSS) as an optimal solution for MSC problem, and devise the elitist directive breeding (EDB) method to get PSS solutions. In order to speed up the search efficiency and improve the feedback recall, we then develop the fast EDB (FEDB) algorithm, which is able to return top-${k}$ranked results according to different searching requirements. To demonstrate the effectiveness of COF mechanism, we conduct extensive simulations based on a virtual mobile sensing scenario. The results show that our proposed COF mechanism outperforms the state-of-the-art solutions significantly in terms of search efficiency and feedback quality, and specially, FEDB responds quite faster than EDB under the command of top-${k}$ranking.

Using small cell base station (SBS) with unmanned aerial vehicle (UAV) as a carrier becomes a promising solution for areas with high-density mobile users. On the other hand, 5G network would apply the LTE technology into the unlicensed spectrum, named Licensed-assisted Access (LAA), due to the limitation of licensed band. In this paper, we propose to utilize LAA technology into the UAV to expand available transmission band. By focusing on the transmission experience of very important (VIP) users, we propose an enhanced LAA (eLAA) technology, which integrates LAA into the IEEE 802.11e protocol. Under the proposed UAV enabled eLAA network, our goal is to maximize the energy efficiency of the on-board communication device through a joint trajectory design and resource allocation strategy. The proposed nonlinear fractional problem has been solved by the Dinkelbach-type algorithm and the block coordinate descent (BCD) mechanism. Numerical results demonstrate the effectiveness of our proposed scheme.

Technological advances allow perceptual physical objects to be connected to the Internet and share their information over webpages. As a predominant source of public sensor data, automatic discovery of these data is critical and desirable for general Internet of Things search service, which is fundamental to many intelligent applications. However, discovering sensor data from webpages is quite challenging, since there are diverse data presentation modes, and webpage layouts and structures are complicated and heterogeneous. To this end, we explore webpages to discover and collect sensor data under a hierarchical mechanism. In this paper, we first devise novel textual features (TFs) to recognize potential webpages that may contain sensor data; specifically, we construct sensing information corpus to provide keyword reference for the features. Then to get the position of sensor data, we develop granularity adaptive page segmentation (GAPS) algorithm to segment potential webpages into a set of informative blocks; and accordingly, we extract several visual features (VFs) of the blocks so that sensor data can be identified via a block classifier. Based on the novel created sensing information pages dataset, which consists of webpages and manual markings about sensing data, extensive experiments are conducted to evaluate the performance of our exploration methods. Results demonstrate that the TFs achieve supreme performance in sensing data recognition when compared to the state-of-the-art approaches, and GAPS is efficient to locate multimodal sensor data in cooperation with the VFs.

Device-to-Device (D2D) networks impose a significant challenge on delay-sensitive crowdsourcing due to the highly nondeterministic and intermittent network connectivity. Under this setting, the paper investigates a participant recruitment problem in which an initial set of recruited nodes, which we call seeds, need to make an optimal decision on what other nodes to recruit to perform the crowdsourcing task. These seeds face the dilemma that recruiting more nodes increases their own payment but on the other hand also increases the risk of being excluded from the crowdsourcing task. As a first attack to this problem, we propose a dynamic programming algorithm. However, it is a centralized solution and hence the practicality is compromised. Therefore, we introduce two distributed alternatives. One is based on the divide-and-conquer paradigm by first partitioning a network into a set of opportunistic Voronoi cells and then running an optimization algorithm in each cell. The other is a task-splitting scheme which recursively delegates the recruitment task to newly joined nodes. We implemented our proposed solutions on an Android-based prototype and built a testbed using 25 Dell Streak tablets. Our experiments which lasted for 24 days demonstrate that the distributed schemes approximate the theoretical optimum with affordable complexity. Moreover, we conducted simulations with a much larger scale and more diverse settings. The simulation results corroborate the experimental data and confirm that our proposed distributed solutions closely approach the performance of the centralized solution while satisfying the optimization goal under different network configurations.

In this work we study the problem of delay-constrained data transmission in mobile opportunistic networks. In contrast to the single-copy single-path and multi-copy multi-path routing schemes that have been discussed in the literature, we aim to determine an optimal single-copy multi-path transmission strategy that satisfies delay requirement and at the same time minimizes communication cost. We first propose a centralized optimal formulation, and then develop a distributed routing algorithm under practical network settings. We implement the proposed algorithm on Dell Streak tablets and carry out an experiment with 25 nodes for a period of two weeks. Moreover, we extract the algorithm codes from our prototype and run simulations based on the Haggle trace to study its performance trends under various network settings.

Underwater acoustic sensor networks (UASNs) have been put significant attention recently from both academia and industry for undersea resources exploring and of scientific data gathering in underwater environments. The important characteristic of an underwater acoustic sensor network is that, most underwater acoustic sensor nodes have a certain beam width and a three-dimensional direction, is ignored by the existing underwater routing protocols. This characteristic will reduce the network connectivity and cause a large number of asymmetric links, so it will lead to the sharp decline of the existing protocol performance. We develop a Beam width and Direction Concerned Routing protocol (BDCR) to tackle this problem in UASNs. A key advantage of our protocol is that it can achieve relatively high packet delivery rate and ensure reasonable energy consumption when take into account the impact of the beam width and three-dimensional direction. The proposed protocol is compared with a representative routing protocol for UASNs. The simulation results verify the effectiveness and feasibility of the proposed work.