Edge Networks (ENs) are emerging networks that enable deploying multiple virtual network functions (VNFs) on resource-limited edge servers to provide users with tailored virtual network services. Decomposing a single VNF into multiple thinner replicas can enhance service reliability while inevitably incurring additional computing capacity consumption (e.g., operating system overhead caused by instantiating more replicas), which increases with the number of decomposed replicas. Moreover, redundant backup replicas can be deployed near the replicas to enhance the reliability further. However, the stateful nature of VNFs requires state synchronization among replicas and between replicas and backup replicas, resulting in additional communication traffic. In this paper, we consider a joint strategy for the decomposition and deployment of stateful VNFs with the goal of minimizing total cost while meeting users’ reliability requirements. The total cost includes the computing cost for instantiating replicas and backup replicas, the additional consumption of computing capacity due to VNF decomposition, and the communication cost for routing traffic among users, replicas, and backup replicas. We first formulate the cost minimization problem as an integer nonlinear program and prove that it is NP-hard. Then, we propose an online two-stage scheme to solve this problem, where the first stage is a VNF decomposition algorithm, and the second stage is a deployment algorithm based on deep reinforcement learning (DRL). The former effectively reduces computing cost by iteratively adjusting the number of replicas and backup replicas, while aiding the latter to adaptively minimize communication cost. Extensive experiments demonstrate that our scheme is promising compared to existing state-of-the-art methods.

Vehicular edge computing (VEC) is an emerging computing paradigm that is rapidly advancing the development of the Internet of Vehicles (IoV). However, edge server has limited data storage capacity and computing resource, making it difficult to handle the massive offloading requests from IoV applications. Moreover, the mobility of vehicles and dynamic data traffic make it highly challenging to design optimal offloading and resource allocation strategies. To address the challenges mentioned above, we design a cloud-edge–vehicle hierarchical architecture for IoV task offloading, introducing a cloud server to assist in computation and alleviate the overload pressure on edge server. Considering the impact of vehicle mobility on task offloading, we propose a mobility detection method to predict which vehicles might leave the communication range of the base station, thereby preventing task offloading failures. Additionally, to achieve efficient task offloading and resource allocation in this complex IoV system, we propose a multiagent-reinforcement-learning-based vehicle proposal offloading algorithm (MVPOA). This algorithm enables vehicles to autonomously decide whether to process tasks locally or propose offloading to edge server. The edge server then decides whether to accept offloading requests based on task priority and sends rejected tasks to cloud server for processing, thereby maximizing the utilization of resources at each layer of the system. Simulation results demonstrate that MVPOA outperforms other baseline approaches in optimizing system delay and energy consumption.

Edge intelligence is an emerging paradigm in edge computing that deploys Deep Neural Network (DNN) models on edge servers with limited storage and computation capacities to provide inference services for high mobility and real-time applications, such as autonomous driving or smart surveillance, with varying accuracy and delay requirements. Adapting application configurations (e.g., image resolution or video frame rate) while selecting different DNN models and deployment locations can provide high-accuracy, low-delay inference services that meet user requirements. However, the configurations and DNN models of various inference services are highly heterogeneous. As balancing inference accuracy, resource cost, and delay is a multi-objective programming problem, it is a great challenge to obtain the optimal solution. To address this challenge, we propose a novel online framework to jointly optimize the configuration adaption, DNN model selection, and deployment for heterogeneous inference services. Specifically, we first formulate this joint optimization problem as an integer linear programming problem and prove it is NP-hard. Then, we further model the problem as a Partial Observable Markov Decision Process (POMDP) and solve it by developing a Heterogeneous-Agent Reinforcement Learning (HARL) based algorithm, named Heterogeneous Inference Service ProvidER (HISPER). It allows agents to have different action spaces corresponding to different types of configurations and DNN models. Finally, extensive experiments demonstrate that the proposed algorithm outperforms other state-of-the-art counterparts.

Edge-enabled Vehicular Metaverse (EVM) is a new paradise supported by various compute-intensive Virtual Vehicle Services (VVSs), where users can immerse and enjoy their spiritual world. User immersion is critical during VVS provisioning in the EVM, yet it can be weakened or curtailed by a sense of disengagement caused by unknown failures. Providing redundant backups VVSs (BVVSs) and keeping the Age of Backup Information (AoBI) could effectively resist and avoid this disengagement when failures occur. However, the trajectories of mobile vehicles are unknown and dynamic, which makes it challenging to optimally migrate VVSs and BVVSs or adjust the update frequency of backup information in real-time, so as to ensure service reliability and AoBI while minimizing the cost of accepting VVS-based metaverse services. In this paper, the above long-term issue is first decomposed into discrete single-slot sub-problems that are modeled as integer linear programming problems. Then, a comprehensive resource explorer named spotlighter is designed, where the first and second parts are a metaverse service home prediction algorithm based on deep learning and a VVS migration algorithm based on randomized rounding, respectively. By tracking the dynamical locations of service homes based on current and historical information, the former can help the latter to adaptively minimize migration costs on VVS re-instantiation and traffic transmission among services and moving vehicles. Finally, a cost-adaptive AoBI guarantee algorithm is merged in spotlighter to ensure the freshness of backup status, by trading-off synchronization cost on BVVS migration, backup update, and backup synchronization. Theoretical analyses and experiments based on real databases show that our algorithms are promising compared with baseline algorithms.

With the rapid development of edge intelligence cloud (EIC), mobile users are not satisfied with a single artificial intelligence inference service, but require multiple inference services with chain dependencies to process data. Each AI service chain (AISC) is provided as a series of interconnected virtual network functions (VNFs) on-demand deployed on edge servers. However, AISCs experience unpredictable failures and potential attacks in EIC, which may violate different inference requirements of mobile users for reliability, security, and accuracy. How to optimally deploy VNFs and BVNFs on trusted edge servers, and select secure links to form satisfactory AISCs, meanwhile throughput of receiving requests is maximized while deployment cost of computing resources used to create VNFs and BVNFs with different model sizes is minimized in real-time, is a challenging problem. In this paper, the problem is first formulated as an integer linear programming and proved to be NP-hard. Then, we consider the problem under two online backup scenarios: one is an on-site scenario where AISC requests from the mobile devices arrive one by one, and link securities between VNFs and corresponding BVNFs are ignored because they are always on the same edge server; another is an off-site scenario where a set of AISC requests are given, and VNFs and BVNFs are deployed on different servers. Finally, two online algorithms with provable competitive ratios are proposed to solve the above two problems in polynomial time. Theoretical analyses and experiments based on real network topologies demonstrate that our algorithms are promising compared to baseline algorithms.

Bidirectional service function chain (BSFC) consists of multiple virtual network functions (VNFs). Through VNF deployment and link mapping, BSFCs can be embedded into resource-constrained mobile edge networks to provide low-latency network function services to users participating in interactive applications such as multi-player online games. Data from these users are routed through BSFCs to the edge node where the application is located for interaction and then returned to the users through the BSFCs, thus enabling synchronization among multiple users. However, the edge nodes or links have limited computing or bandwidth resources to serve only a fraction of users simultaneously. Therefore, the embedding decisions among different users can affect each other. In this paper, we propose a novel BSFC embedding strategy for interactive applications with the goal of minimizing computing and bandwidth resources while satisfying users' latency requirements. We first model the BSFC embedding problem as an integer nonlinear programming problem. Then, by closely examining the complexity of the problem, we propose a distributed algorithm based on game theory. We theoretically analyze the properties of the proposed algorithm and show that it can obtain a solution with a worst-case performance bound. Finally, extensive experiments show that the proposed algorithm outperforms several existing algorithms.

Mobile edge Industrial Internet of Things (MEIIoT) is composed of Industrial Internet of Things (IIoT) and mobile edge computing, which is currently a new type of IIoT. MEIIoT has the characteristics of large scale and strong dynamics (e.g., network topology or number of IIoT devices would change from time to time). The placement of virtualized network functions (VNFs) in MEIIoT refers to placing multiple network functions (e.g., motion analyzer and video processor) on edge nodes in a form of software instances, so that IIoT devices can flexibly obtain services of these VNFs. However, an edge node can only be placed a small number of VNFs, because of its limited storage and computing resources. Therefore, if an IIoT device requires multiple VNFs, it needs to transmit its data to access several edge nodes, which would cause high delay. How to optimally place all the VNFs on edge nodes in MEIIoT, so that the whole access delay for all IIoT devices that requiring VNFs is minimized, is a challenging problem. In this article, we design an online placement algorithm. First, we decompose a long-term VNFs optimization problem into a series of one-shot optimization problems. Second, we formulate these one-shot problems into integer nonlinear programming problems, and prove that they are NP-hard. To overcome this hardness, we then propose a heuristic algorithm. Finally, we carried out extensive experiments with real-world datasets to validate the efficacy of our proposed solution.

Mobile edge computing (MEC) has emerged as a promising computing paradigm that provides flexible and responsive local services for mobile user equipment at the network edge. Software instances for user equipment tasks are typically deployed as Virtualized Network Functions (VNFs) at resource-constrained edge nodes. Task data exchanged across the VNFs in serial can incur high task completion latency. It is therefore desirable to deploy certain VNFs in parallel. However, deciding where to deploy VNFs depends on which VNFs are parallel, and conversely, their deployment also affects their parallel execution. In this paper, for the first time, we jointly consider the parallelization and deployment strategies for VNFs at edge nodes. We closely examine the complexity of the joint optimization problem and introduce an Improved Service Function Graph (I-SFG) that reflects the coordination and dependency relations among the VNFs to provide parallel services for each piece of user equipment. We first propose an approach based on integer linear programming to find optimal solutions in small-scale scenarios and then present an effective solution through cascading I-SFG construction and VNF deployment approximation to solve large-scale problems. Theoretical analyses and experimental results show the superiority of our joint design and the proposed practical solution.

Large-scale urban pipeline systems (LSUPSs) are complex pipeline networks of flows (e.g., water, oil, or gas). Flow capacity intelligent monitoring, e.g., automatically monitor the sum of flows in an LSUPS, is an important task in smart cities. Recently, mobile sensors and static receiver nodes are used to perform the task. Mobile sensors are released into the network at selected entrances to collect data, and upload the data to receiver nodes deployed at selected locations of the network for further analysis. Due to cost constraints, the numbers of mobile sensors and receiver nodes are limited, which cause the problem that some pipelines may not be monitored. However, applications normally require that some Zones of Interest (ZoIs) in the network have to be monitored. Therefore, how to select optimal entrances and locations for given numbers of mobile sensors and receiver nodes, so that the capacity of monitored flow is maximized within a given time under the constraint that all ZoIs are also monitored with expected probabilities, is a challenging problem. First, we prove the problem is NP-complete. Then, we design two algorithms based on submodular set function optimization to solve it. The first algorithm can obtain an approximate optimal solution with high time complexity, while the second algorithm can obtain a suboptimal solution with much lower time complexity. Finally, we analyze time complexity and approximate ratio of the two algorithms. Theoretical analyses and simulation results show that the proposed algorithms outperform the state-of-the-art algorithms.

Traditional decision making in a vehicle network includes uploading vehicle sensing data to faraway cloud platforms and then returning correlated results to the vehicles. The data have features of large quantity and high redundancy, which causes high communication latency and vehicle applications to deteriorate. Vehicular fog computing (VFC) is a new network paradigm that uses local fog nodes for decision making. However, how to achieve distributed information exchange with low latency is a challenging issue because the connectivity of the vehicle network is low due to vehicle mobility. In this article, a distributed information exchange scheme with low latency in VFC is proposed. First, considering the frequent changes in vehicle positions and the randomness in driving routes, public transportation facilities with a wider driving range such as buses and taxis are used as fog nodes to increase the probability of uploading data. Then, the fog nodes should dynamically adjust the data sampling frequency according to the time-space correlation of the data to ensure that only nonredundant data are received. To minimize the interruption latency caused by accidents during an exchange, the fog nodes evaluate and predict connection states among them and their neighboring vehicles when establishing exchanges. If a fog node finds that a vehicle cannot complete information exchange because the vehicle may move outside its communication range in a future period, it will recalculate an optimized relay route for the vehicle by using mixed integer programming. Theoretical analysis and simulation results show that compared with the existing work, the proposed scheme can completely exchange all vehicle data with lower latency.

Large-scale acyclic flow systems (LSAFSs) are models of pipeline networks that are used to transport important resources, such as water, oil, and natural gas in smart cities. LSAFSs have features of complex topology and deep-underground deployment, which would cause accidents, such as leakages and pollution that are difficult to be detected in time. Mobile sensors (MSs)-based monitoring schemes appear as an effective solution in recent years to handle this situation. These schemes drop MSs into an LSAFS from specified locations, and the MSs will move along with fluid inside the LSAFS to collect data. When the MSs pass through a predeployed and activated receiver node (RN), they will upload their data to the RN. However, how to decide the optimal locations and timings for dropping of the MSs and the best activation periods and deployment locations of the RNs, such that total length of monitored pipelines in the LSAFS is maximized, as well as energy consumption of the RNs is minimized, is a challenging problem. The problem is more challenging if potential uploading failures and undetermined movement of the MSs are considered. In this article, we first formalize the problem as a multiobjective optimization problem. Then, we decompose the problem into a submodular optimization problem and an union set optimization problem, and prove they are NP-hard. Next, an approximate algorithm based on the Pigeonhole principle is proposed to solve the first problem, and a heuristic algorithm based on the inclusion-exclusion principle is proposed to solve the second problem. Extensive theoretical analyses and simulations show that the proposed algorithms outperform state-of-the-art algorithms.

Nowadays, sensor-cloud systems have received wide attention from both academia and industry. Sensor-cloud system not only improves performances of wireless sensor networks (WSNs), but also combines different functional WSNs together to provide comprehensive services. However, a variety of malicious attacks threaten the sensor-cloud security, such as integrity, authenticity, availability and so on. Traditional available security mechanisms (e.g., cryptography and authentication) are still vulnerable. Although there are schemes to provide security by trust evaluation, the evaluation considers whether or not a sensor is credible only by checking the communication behaviors. Furthermore, when mobile sensor sinks are employed to collect sensing data, there appears a type of attacks called replicated sink attacks that are often ignored in the previous work. These attacks may bring serious vulnerability to trustworthy data collection in sensor-cloud systems. In this paper, we propose a comprehensive trustworthy data collection (CTDC) approach for sensor-cloud systems. Three kinds of trust, i.e., direct trust, indirect trust, and functional trust are defined to evaluate the trustworthiness of both sensors and mobile sinks. Except for resisting malicious attacks, the performances of sensor-cloud, such as energy, transmission distance and network throughput are also considered. We also conduct extensive simulations to evaluate the efficiency of CTDC. The simulation results show that CTDC correctly identifies malicious nodes and offers an improved performance in the data collection.

Fault-Prone Mobile Edge Cloud (FP-MEC) is a new type of distributed network composed of mobile edge computing and network function virtualization, where virtual network services can be provided in the form of service function chains (SFCs) that are a sequence of virtual network functions (VNFs) on-demand deployed on resource-limited edge servers. FP-MEC has a characteristic that the fault probability of each VNF is dynamic and fluctuates with time and workloads, making SFCs temporarily unreliable. To increase the reliabilities, redundant Backup VNFs (BVNFs) need to be deployed near the VNFs and activated when they experience faults. Different mobile users would request different SFCs with reliability and service time demands to process their data. However, workloads of VNFs are dynamic and unpredictable in FP-MEC due to random arrival of user requests. How to optimally deploy VNFs and corresponding BVNFs on a set of edge servers to form expected SFCs that have higher reliabilities than user demand values, meanwhile throughput of receiving requests is maximized while receiving cost is minimized in real-time, is a challenging problem. The receiving cost is composed of deployment cost of instantiating VNFs and BVNFs, and communication cost of routing data among users, VNFs and BVNFs. In this paper, the long-term provisioning problem is first formulated as an integer linear program and proved to be NP-hard. Then, it is discretized into a sequence of one-slot optimization problems to handle practical time-varying fault probability, where a set of SFC requests are given at each time slot, and receiving or rejecting decisions are executed immediately without any future information. Finally, an online approximation scheme with a constant approximation ratio is proposed to solve the one-slot problems in polynomial time. Theoretical analyses and experiments based on real network topology of CERNET in China demonstrate that the scheme is promising compared to existing works.

In smart cities, data monitoring for basic infrastructures, such as an urban water supply pipeline system or an oil/gas supply pipeline system, has become one of the most important tasks. An urban pipeline system that can be called a large-scale acyclic directed pipeline network (LSADPN), which has some characteristics, such as wide geographical distribution, complex connection, and deep underground. Therefore, it is difficult to be monitored comprehensively and accurately in real time. In recent years, some studies have proposed methods that use mobile sensors that are put into a pipeline network to obtain accurate monitoring results from the interior of the network. However, the mobile sensors have no motion devices and can only flow with the liquid in the pipelines. When a pipeline connection is encountered, it is uncertain whether all branches of the connection can be covered. Therefore, the maximum monitoring capacity, i.e., liquid capacity in the network monitored by the mobile sensors, is difficult to be maximized. In this article, the problem of maximizing the monitoring capacity of LSADPN is first proved to be NP-hard. Then, two new mobile sensors deployment algorithms based on the submodular function optimization method are proposed. Theoretical analyses and experimental results show that the two algorithms can monitor a whole network with high probability and achieve near maximum monitoring capacity with a specified number of mobile sensors and a given time.

With the emergence of the fog computing and the sensor-cloud computing paradigms, end users can retrieve the desired sensory data generated by any wireless sensor network (WSN) in a fog-based sensor-cloud system transparently. However, the fog nodes and the cloud servers may suffer from many kinds of attacks on the Internet and become semitrusted, which threatens the security of query processing in the system. In this paper, we investigated the problem of secure, fine-grained spatial-temporal Top- k query in fog-based mobile sensor-cloud systems (FMSCSs) and proposed a novel scheme named STQ-SCS to tackle the problem based on the virtual grid construction and the size-order encryption-binding techniques. STQ-SCS can preserve the privacy of the sensed data items and their scores and make end users verify the completeness of the query results of fine-grained spatial-temporal Top- k queries with a 100% successful rate even if the fog nodes and the cloud servers are not totally trustworthy. Besides the good security performance, simulation results indicate that STQ-SCS is also an efficient scheme that incurs a much lower communication cost than the state-of-the-art schemes on securing fine-grained spatial-temporal Top- k query in FMSCSs.

Social sensor cloud (SSC) is combined with social network, wireless sensor network, cloud computing, and fog computing, which is currently a new type of Internet of Things (IoT). In order to provide a convenient, open, and highly reliable SSC services, the devices of fog computing are distributed at the edge of cloud computing. The devices of fog computing can independently process and store data, and feedback more quickly in SSC. The sensing layer of SSC faces different types of physical attacks and communication attacks, such as message forgery, message tampering, reply attacks, hidden data attacks, etc., lead to the lack of trust between social sensors and cloud data centers in SSC. Therefore, the trust evaluation between the sensing layer and the network layer is necessary. However, computing the reliability of the social sensor data in cloud data centers will generate a large amount of trust computing overhead, communication overhead, and communication delay, which hinder the widespread application of SSC services. To combat this issue, a reliable trust computing mechanism (RTCM) based on multisource feedback and fog computing fusion is proposed. First, a new metric is designed for the trust of social sensor nodes, and multisource feedback trust value collection is performed at the sensing layer to improve the detection of malicious feedback nodes. Second, the trust feedback information of the sensing layer is collected by the devices of fog computing, and the recommendation trust calculation is performed, which reduces the communication delay and computing overhead. Third, a fusion algorithm is designed to aggregate different types of feedback trust values, which overcomes the limitation of trust weights in artificial weighting and subjective weighting in traditional trust mechanisms. Theoretical analyses and simulation results show that the proposed trust computing mechanism has better computational efficiency and higher reliability compared with existing methods.

Recently, much attention is paid to multi-robot systems due to their widespread applications such as warehouse robotics, persistent surveillance, and exploration of unknown environments. Although urgently required by the applications, coordination among multiple robots remains to be challenging. Among the problems of multi-robot coordination, pattern formation serves a fundamental one. It aims to control a group of robots to form a desired shape with some certain goals such as best formation quality, minimum makespan or minimum total distance. Existing works mainly focus on the formation of certain patterns, such as repeating squares or a circle. those approaches cannot be generalized to arbitrary pattern formation. In this paper, we propose a decentralized algorithm for a multi-robot system to generate a given formation with an arbitrary repeating pattern. We introduce basic pattern graph and assembling graph to define a repeating pattern and formation quality for measurement. Towards solving the repeating pattern formation problem, our approach is divided into two phases. The robots are grouped into multiple basic patterns in the first phase, and the patterns are assembled level by level in the second phase. Simulations and real-world experiments indicate the effectiveness and practicability of our approach.

A novel network paradigm of edge computing, namely, two-tiered wireless sensor networks (TWSNs), has been proposed by researchers in recent years for its high scalability and robustness. However, in the TWSNs-based edge computing systems, the storage nodes, which are located at the upper layer of the systems, are prone to be attacked by adversaries because they play a key role in bridging sensor nodes and Sink, which may lead to the disclosure of all the data stored on them as well as some other potentially devastating results. In this article, we study the integrity-and-privacy preservation problem for spatial- temporal Top-k queries in the TWSNs-based edge computing systems and propose a sequence-encryption-based lightweight scheme named sequence-encryption-based lightweight scheme for securing spatial-temporal Top-k queries (SLS-STQ) to solve the problem. In SLS-STQ, three algorithms, namely, the report preparation algorithm, the query processing algorithm, and the integrity verification algorithm, are designed for the sensor nodes, the storage nodes, and Sink, respectively. The theoretical analysis shows that SLS-STQ is able to achieve both integrity validation and privacy preservation with low computational complexity, and the simulation results show that SLS-STQ is much more efficient than the related state-of-the-art schemes.

The ubiquity of ever-more-capable mobile devices, especially smartphones, brings forth participatory sensing to collect and interpret information. It can achieve unprecedented quantity of data. However, it is arduous to guarantee quality of data because everyone can contribute data without scrutinization. It is an important issue in quality-oriented participatory sensing. Our idea to address this issue is motivating participants to contribute accurate data for improving data quality directly. In this article, we propose a reputation-based incentive mechanism, RIM, to realize the idea. More specifically, we identify the participants who collect the accurate data and regard them as the reputable ones. Then, the reputable participants are granted a higher chance to obtain rewards so that other people will try to follow such users and become reputable as well. Namely, RIM can encourage and steer users to collect accurate data in the long term. We analyze our incentive mechanism by formalization and premise implications. For a feasibility study of participatory sensing and verification of the implications, we implement and deploy a participatory sensing application focusing on monitoring environmental noise in a specific location as a case study and conduct a simulation based on the case study to further evaluate the proposed incentive mechanism. The results from the case study and the simulation present that RIM can remarkably increase the quality of collected data in participatory sensing while corroborating our theoretical implications.

Privacy is a pivotal issue of mobile apps because there is a plethora of personal and sensitive information in smartphones. Many mechanisms and tools are proposed to detect and mitigate privacy leaks. However, they rarely consider users' preferences and expectations. Users hold various expectation towards different mobile apps. For example, users may allow a social app to access their photos rather than a game app because it goes beyond users' expectation to access personal photos. Therefore, we believe it is practical and beneficial to understand users' privacy expectations on various mobile apps and help them mitigate privacy risks introduced by smartphones. To achieve this objective, we propose and implement PriWe, a system based on crowdsourcing driven by users who contribute privacy permission settings of the apps installed on their smartphones. PriWe leverages the crowdsourced permission settings to understand users' privacy expectations and provides app specific recommendations to mitigate information leakage. We deployed PriWe in the real world for evaluation. According to the feedback of 78 users who evaluated our system and 422 participants who completed our survey, PriWe is able to make proper recommendations which can match participants' privacy expectations and are mostly accepted by users, thereby help them to mitigate privacy disclosure in smartphones.

People nowadays almost want everything at their fingertips, from business to entertainment, and meanwhile they do not want to leak their sensitive data. Strong information protection can be a competitive advantage, but preserving privacy is a real challenge when people use the mobile apps in the smartphone. If they are too lax with privacy preserving, important or sensitive information could be lost. If they are too tight with privacy, making users jump through endless hoops to access the data they need to get their work done, productivity can nosedive. Thus, striking a balance between privacy and usability in mobile applications can be difficult. Leveraging the privacy permission settings in mobile operating systems, our basic idea to address this issue is to provide proper recommendations about the settings so that the users can preserve their sensitive information and maintain the usability of apps. In this paper, we propose an unobtrusive recommendation system to implement this idea, which can crowdsource users' privacy permission settings and generate the recommendations for them accordingly. Besides, our system allows users to provide feedback to revise the recommendations for getting better performance and adapting different scenarios. For the evaluation, we collected users' preferences from 382 participants on Amazon Technical Turks and released our system to users in the real world for 10 days. According to the study, our system can make appropriate recommendations which can meet participants' privacy expectation and mobile apps' usability.

Traditional tracking solutions in wireless sensor networks based on fixed sensors have several critical problems. First, due to the mobility of targets, a lot of sensors have to keep being active to track targets in all potential directions, which causes excessive energy consumption. Second, when there are holes in the deployment area, targets may fail to be detected when moving into holes. Third, when targets stay at certain positions for a long time, sensors surrounding them have to suffer heavier work pressure than do others, which leads to a bottleneck for the entire network. To solve these problems, a few mobile sensors are introduced to follow targets directly for tracking because the energy capacity of mobile sensors is less constrained and they can detect targets closely with high tracking quality. Based on a realistic detection model, a solution of scheduling mobile sensors and fixed sensors for target tracking is proposed. Moreover, the movement path of mobile sensors has a provable performance bound compared to the optimal solution. Results of extensive simulations show that mobile sensors can improve tracking quality even if holes exist in the area and can reduce energy consumption of sensors effectively.

In some harsh environment, wireless sensor networks without the sink are often deployed. In the network, the nodes just have limited energy and are easy to fail. In order to prevent the data loss due to the failure of nodes, each node disseminates its data to be stored at a subset of nodes in the network for preservation. However, each node just knows the information of its neighbors, and just has limited storage space. Therefore, it is a challenge to manage the processes of data dissemination and storage effectively. In this paper, an adaptive probability broadcast-based protocol, named APBDP (Adaptive Probability Broadcast-based Data Preservation), is proposed to tackle the challenge. In APBDP, each node disseminates its data to the network by an adaptive probability broadcast mechanism. The mechanism can not only enable all nodes receive the data packet, but also reduce the redundance of data transmission to conserve the energy of nodes. Moreover, each node stores the data received by using LT (Luby Transform) codes, which are the first rateless erasure codes that are very efficient as the amount of data grows. After above processes are finished, a collector (e.g., a motor vehicle) can recover all data by visiting a small subset of nodes. To the best of our knowledge, APBDP is the first scheme that uses adaptive probability broadcast to achieve the efficient data preservation. Theoretical analyses and simulations show that APBDP can achieve higher performance of data preservation and energy efficiency than existing protocols.

How to improve the data persistence, i.e., the availability of all source data, is an important issue in wireless sensor networks. The issue requires that each source node can disseminate its data (packet) to a subset of nodes in the network for effective storage. In this paper, a distributed scheme based on LT(Luby Transform)-codes, named LTSIDP, is proposed. LT codes are the first rateless erasure codes that are very efficient as the amount of data grows. In LTSIDP, each node uses overhearing to get information whether a packet has been transmitted by one of its neighbors. When a node needs to transmit a packet, it randomly chooses one of its neighbors that does not transmit the packet as receiver. On the other hand, each node can compute a key parameter of LT codes by using some properties of the packet transmission mechanism, and then store the data accordingly. After the process of storage is finished, a collector (e.g., a motor vehicle) can recover all data by visiting a small subset of nodes. To the best of our knowledge, LTSIDP is the first scheme that uses overhearing to improve the data persistence. Theoretical analyses and simulations show that LTSIDP can achieve higher data persistence and energy efficiency than existing schemes.

Data gathering is a broad research area in wireless sensor networks. The basic operation in sensor networks is the systematic gathering and transmission of sensed data to a sink for further processing. The lifetime of the network is defined as the time until the first node depletes its energy. A key challenge in data gathering without aggregation is to conserve the energy consumption among nodes so as to maximize the network lifetime. We formalize the problem of tackling the challenge as to construct a min-max-weight spanning tree, in which the bottleneck nodes have the least number of descendants according to their energy. However, the problem is NP-complete. A ¿(log n/log/log n)-approximation algorithm MITT is proposed to solve the problem without location information. Simulation results show that MITT can achieve longer network lifetime than existing algorithms.

In some delay-sensitive and durative surveillance applications, in order to gather data at each round, all nodes in wireless sensor networks are organized as a tree rooted at the sink. The tree should be designed carefully to meet the challenges of constraining the data gathering delay and maximizing the network lifetime. The problem of constructing the tree is NP-complete. Moreover, a contradiction between the two challenges is proved in this paper. A novel delay-constrained and maximum lifetime data gathering Algorithm, named DCML, is proposed to solve this problem. DCML needs not to know the location of nodes, and it can construct an energy-balanced tree with limited height at each round. Theoretical analyses and simulation results show DCML can not only achieve longer network lifetime than some existing algorithms, but also constrain the data gathering delay in the network effectively.