Unmanned Aerial Vehicles (UAVs) are increasingly deployed for time-sensitive data gathering in wireless sensor networks (WSNs). Limited onboard energy and buffer capacities, however, present major challenges for mission continuity and service coverage. This paper proposes a two-tier UAV deployment (2T-UAV) scheme with a cooperative design of two UAV roles: mission-oriented UAVs are responsible for collecting prioritized data, and ferry-oriented UAVs provide in-air energy charging and buffer offloading services. The 2T-UAV mechanism determines a mission-oriented UAV’s hovering routes by a Weighted Traveling Salesman Problem (W-TSP) method with an importance factor of data urgency. In addition, the 2T-UAV scheme develops a Nonlinear Least Squares-based Route (NLLSR) method for optimizing ferry-oriented UAV’s recharging routes. After extensive simulation with both synthetic and real-world map scenarios, performance examination shows that the 2T-UAV scheme significantly reduces mission time, energy consumption, and packet drop rate in comparison with several geometry-based, TSP-based, and reinforcement learning-based methods. Results highlight the feasibility and benefits of mission- and ferry-UAV cooperation and enhanced route planning methods for efficient data collection in next-generation UAV-assisted WSNs.

Offering traffic safety information to drivers and passengers is one of essential services towards the smart city. Recent research utilizes AI models to analyze the collection of IoT-driven data in transportation environments. Exploring unveiled characteristics of traffic information to improve traffic control and accident prevention on roads, this way becomes plausible. Prior studies exploited various sorts of spatio-temporal traffic data to achieve the traffic prediction using deep learning models. Without understanding the complexity of spatio-temporal data, however, their efforts have not fully shown the effectiveness of deep learning-based traffic prediction and risk presentation. In this paper, our study first applies the Pearson correlation coefficient to clarify that traffic accidents appear in high correlation with time and space patterns. We identify multiple features from traffic domains, and employ CNN first and then LSTM learning techniques on several volumes of spatio-temporal traffic data, including weather, time, traffic flow, and historical traffic accidents and locations, etc. Our study shows that the combination of CNN and LSTM learning on spatio-temporal traffic data is applicable and useful for traffic risk prediction. Under experiments and demonstrations with actual traffic datasets, our proposed traffic risk prediction scheme, called CLwST, can exhibit more accurate results, faster convergence and lower loss in comparison with the two prior studies based on LSTM and ConvLSTM schemes.

Mobile crowdsensing technologies augment the collective effort on exploiting data from a large crowd of mobile users in ubiquitous environments. When mobile users partake in executing crowdsensing tasks, they can receive rewards and be incentified to stay in virtual teamwork. This paper proposes a game-based incentive mechanism, named Incentive-G, aiming at recruiting mobile users effectively and improving the reliability and quality of sensing data against untrusty or malicious users. The Incentive-G mechanism consists of several design phases, including analyzing sensing data, determining reputations of mobile users, and ensuring data quality and reliability by voting in a task group. This mechanism adopts a two-stage Stackelberg game for analyzing reciprocal relationship between service providers and mobile users, and then optimizes incentive benefits using backward induction. Our analysis shows that the existence and uniqueness of the Stackelberg equilibrium can be validated by identifying the best data-provision strategies for mobile users. In addition, the maximum revenue strategy for a service provider can be found by gathering a sufficient amount of high-quality data from mobile users. Performance results manifest that the Incentive-G mechanism is able to significantly encourage mobile users to contribute their efforts and maximize the revenue for game-based crowdsensing services.

Abstract Multi‐access edge computing provides computation and network resources in proximity to user applications in mobile environments. Deploying edge servers in network boundary can not only offload the heavy task loading on the cloud, but also alleviate resource‐limited capabilities of mobile devices. Rather than many stand‐alone edge servers, the concept of multi‐server edge computing is recently advocated to contend with the issues of system scalability and service quality against dynamic task workload. This study exploits collaborative computing resources and designs a task migration strategy for multiple edge servers in mobile networks. This study formulates a queueing optimization problem of minimizing the overall service time in a multi‐server system. An intelligent task migration scheme is then developed using the deep reinforcement learning and Q‐learning techniques. With a variety of numerical attributes derived from the queueing model, this intelligent scheme can arrange the task distribution among edge servers to enhance the task processing capability. Simulation‐based results show that the proposed task migration scheme can sustain service efficiency and resource utilization, which is promising as compared with conventional designs without collaborative intelligence in mobile environments.

As the coming maturity of Internet of Things, many home-networked devices with various sensors are deployed in residential environments. It is important to accommodate many devices that compete for bandwidth allocation to transmit data inwards and outwards through a certain home network domain. This paper proposes a novel mechanism which can adjust media stream qualities and transmission bandwidth allocations to different devices. This mechanism is agile against dynamic changes of data workload and finite network resources between inside and outside of a home network. Practical demonstration exhibits that the effects are able to maintain differentiated media transfer services in IoT-based home networks.

When sparse and challenged networks are characterized by small node population, non-randomized node distribution, and low node density, it is hard to maintain end-to-end connectivity among mobile nodes. The mobile opportunistic networking technology provides data dissemination services by means of transient inter-node communications during node movements in a network. Recent studies intend to exploit node mobility to store, carry and forward data upon opportunistic contacts among nodes. To address the uncertainty nature of future contacts, traditional ways replicate message copies to increase data delivery. However, most replication techniques forward message copies either in a greedy manner or select the next forwarding node by considering only contact history. In this paper, we exploit contact patterns among mobile nodes and propose a Regular and Sporadic Contact-Based Routing (RSCR) scheme where regular and sporadic contact patterns are defined to distinguish contacts of which either periodically or occasionally appear in a time scale, to enhance delivery rate in a cost-effective manner. We conduct synthetic simulation to examine the RSCR scheme under the SLAW and Infocom'05 mobility traces. Performance results show its efficiency on the successful delivery rate with lower message overhead as compared with the typical Epidemic and PRoPHETv2 schemes.

This paper addresses QoS routing in an integrated network architecture based on the software-defined networking and edge computing technologies. Our study leverages the benefits of edge computing to differentiate data flows of different service types, as well as the programmable features of the software-defined networking to manipulate the forwarding behavior on data flows. Joint QoS-specific factors of latency and packet loss are used to design a novel path selection method for data delivery in such an integrated network. Simulation under heavy traffic patterns shows that our method performs better with lower latency and packet loss rate simultaneously as compared with typical OSPF and some greedy-like routing methods.

Based on the Content-Centric Network (CCN) architecture, a data content can be stored at routing nodes along the transmission path on which the data content transmitted from the content source repository back to the requester. Therefore, while the routing nodes receive the same content requests again, the routing nodes that have stored the queried contents can answer the requests directly by responding with the requested contents from their own storage area. Thereby it eliminates the cost of sending the content requests to the content source repositories again, and thus improves the overall network transmission efficiency. In the CCN network, a routing node refers to the Forwarding Information Base (FIB) of its own forwarding engine - the FIB function is similar to the routing table used in the traditional IP network, for determining where to send out the request packets. This study proposes a Search Approach based on Query Similarity (SAQS), which analyses the information of the temporary data stored in the routing node itself, calculates the weight of the temporarily stored contents for coming query requests, and selects the node that is most likely to store temporarily the queried contents. Finally, through simulation, the proposed SAQS is compared with other query forwarding methods. Validating by different simulation cases, our proposed SAQS exhibits better content search hit rate, while the number of hops and delay time can still remain in satisfactory results.

The mobile opportunistic network is an emerging thread of mobile and wireless networks, which distributes messages when nodes move and encounter with others over infrastructure-less networks. Network topologies in such networks can be fragmented dynamically, and end-to-end paths between any pairs of source and destination cannot be guaranteed. Many studies resort to delay-tolerant, store-carry-and-forward transfer model to deliver messages. However, without regard to contact relationship among nodes, those efforts are sensitive to node mobility and network dynamics. In this paper, we propose a new routing scheme, named Temporal Periodicity for Message Delivery (TPMD). We identify the characteristic of contact periodicity among nodes in a temporal scale, and formulate a period quantification procedure. As a result of temporal periodicity in mobile opportunistic networks, we conduct simulation to examine TPMD and show its efficiency on improving successful delivery rate and lowering message overhead as compared with the famous PRoPHETv2 scheme.

This paper proposes a cluster-based routing scheme for improving cost-effectiveness of message delivery in delay-tolerant networks. The proposed scheme is designed, as follows: (1) every node calculates the expected node density in network proximity; (2) every node perceives whether it stays in a cluster or not; (3) every node can ask for relay nodes to carry messages in a cluster; (4) a network system can control the quantity of message copies by measuring the message density of a cluster to help message distribution in a network. Performance under synthetical simulation shows that this scheme is able to obtain comparable effects in terms of delivery probability and message overhead ratio, particularly when the mobility models are in accord with human behaviour.

The penetration ratio of broadband networks into residential areas increases rapidly by the wide distribution of Internet service providers and networks. People are able to distribute and play various media content with many types of networked multimedia devices for home multimedia entertainment in residential environments. This paper addresses a new idea of home-based IoT networks where home-networked devices are able to communicate with others in a friendly, networked manner instead of traditional manual configurations and wired cabling operations. Accordingly, this paper proposes a novel intelligent media distribution system based on a home-based IoT network. The design of this system integrates UPnP, face recognition, intelligent human-machine interface, and family database technologies. UPnP-compatible HNDs With UPnP, networked devices can discover neighboring devices in a network. Face recognition is incorporated and so provides the UPnP networked devices with the capability of identifying the operating user in front of them. When a user moves in a home-based network, the intelligent human-machine interface allows a user to enforce any media content to be distributed to or displayed onto the UPnP-based device nearby the user. Furthermore, this paper presents a prototypical development, as well as a real demonstration with experimental UPnP-based network devices in home networks. Therefore, the study in this paper enables a ubiquitous media distribution service in home-based IoT network environments.

In delay-tolerant networks where connectivity between nodes is intermittent, replication-based delivery mechanisms are often employed to deliver messages in a store-carry-forward manner instead of end-to-end delivery techniques that are mainly used in traditional computer networks and mobile ad-hoc networks. However, multicasting with replication-based methodologies can induce considerable message traffic and longer delivery delay time, thereby being vulnerable upon system performance. This paper exploits the potential of network coding to design an efficient network-coding multicast mechanism in delay-tolerant networks. In this design, intermediate nodes in a network can perform a specific remix qualification approach which can not only alleviate the dependency problem among coded blocks but also generate more innovative coded blocks for restoring the original message by destination nodes. Simulation results show that the proposed mechanism is able to achieve cost-effective performance as compared with the naive network-coding and the replication-based delivery mechanisms for message multicasting in delay-tolerant networks.

When a catastrophic natural disaster occurs, the efficiency of disaster response operation is crucial to life saving. However, communication systems, such as cellular networks, were usually crashed due to various causes that made coordination difficult for many disorganized disaster response workers extremely. Unfortunately, rapid deployment of many existing emergency communication systems relies on a good transportation system, which is usually not available in a catastrophic natural disaster. We propose an emergency communication system, Contingency Cellular Network (CCN), by connecting disconnected base stations together with wireless links and portable power generators. CCN can support existing mobile phone users with limited capability. Such a system can support a large number of voluntary workers in the early hours of a catastrophic natural disaster, thus saving many lives. Design philosophy and high level system architecture of our framework are presented in this paper.

This paper introduces the RSS Watchdog system, which is capable of news clustering and instant event monitoring over multiple real and online RSS news streams. We briefly mention software architecture design, technical implementation, and prototype demonstration. In addition, the results of real case studies are presented to notice the RSS Watchdog's functionality

Entering and leaving of random peers will result in great variance of data availability in P2P networks, especially when multimedia streams require high priority to the immediate playback data. All peers must be able to receive the complete video segment before that playback. This requirement becomes more critical in mobile and wireless network environments where mobile peers have high frequency to leave and join the P2P network. Therefore, the provision of the tracker in the P2P networks is to accurately track the status of each peer and so to maintain high data availability being considered functionally. Our work deliberates the redundant mechanism of BitTorrent Tracker and designs a BT-like redundant tracker mechanism that is configured to this purpose, and presents the relative performance measure. The result shows that the proposed redundant tracker does not increase overheads and is able to upgrade stability to the playback operations in mobile P2P streaming networks.

The scalability of data broadcasting has been manifested by prior studies on the base of the traditional data management systems where data objects, mapped to a pair of state and value in the database, are independent, persistent, and static against simple queries. However, many modern information applications spread dynamic data objects and process complex queries for retrieving multiple data objects. Particularly, the information servers dynamically generate data objects that are dependent and can be associated into a complete response against complex queries. Accordingly, the study in this paper considers the problem of scheduling dynamic broadcast data objects in a clients-providers-servers system from the standpoint of data association, dependency, and dynamics. Since the data broadcast problem is NP-hard, we derive the lower and the upper bounds of the mean service access time. In light of the theoretical analyses, we further devise a deterministic algorithm with several gain measure functions for the approximation of schedule optimization. The experimental results show that the proposed algorithm is able to generate a dynamic broadcast schedule and also minimize the mean service access time to the extent of being very close to the theoretical optimum.

Modern real-time information dissemination services generate data dynamically and process data requests on demand. Such data or requests are usually valid or useful subject to temporal conditions. This paper devises an efficient on-demand time-critical data broadcast scheme by considering request urgency, service productivity and access fairness, to maintain quality of service and performance. The experimental studies show the proposed scheduling approach is able to achieve low request deadline miss rate and mean access time simultaneously. Its performance is superior to classical EDF and MRF and further better than SIN-k and RxW methods by simply tuning parameters.

This paper presents a fair scheduling scheme for on-line scheduling on-demand time-critical data broadcast in the context of real-time information dissemination. The design of this scheme considers request urgency, service productivity and scheduling fairness and its performance is investigated in terms of request satisfaction and mean access time. The experimental studies show that the proposed scheduling approach is amenable under dynamic traffic of on-demand time-critical requests, and is able to achieve low request deadline miss rate and mean access time simultaneously.

Many modern information applications spread dynamic data objects and process complex queries for retrieving multiple data objects. This paper studies the broadcast scheduling design for dynamic information dissemination. We formulate the problem of scheduling sequential data objects in an online fashion and derive the lower and the upper bounds of mean service access time. Accordingly, we further present several heuristic functions to approximate the schedule optimization. The experimental results show that the proposed design is able to online generate a broadcast schedule whose mean service access time is very close to the theoretical optimum.

The design of a cost-effective location management mechanism is very important in wireless personal communication networks (PCNs). This work presents a backward location update scheme compatible with the location management strategy used in current PCNs. In our design, the visitor location register (VLR) employs an intelligent micro-procedure, which distinguishes different types of location movements and performs different location update procedures. The location information stored in the home location register (HLR) is backward updated on demand necessarily for a call or short message delivery. Thus, the proposed scheme is able to save significant amount of traffic cost and database overhead.

Data dissemination by the use of periodic broadcast is an important scalable data delivery mechanism due to the explosion of wireless information services. Consider that the recent emergence of a multichannel data dissemination model has attracted much research attention. In this paper, we devise an on-line multichannel traffic awareness (MCTA) mechanism by exploiting the notion of client impatience against traffic dynamics in a multichannel data dissemination environment. The design of MCTA performs a cyclical estimation of dynamic access frequency distribution in response to dynamic traffic changes. The experimental results show that MCTA is not only able to attain an estimated distribution of high accuracy, but also of low complexity and robustness without performance degradation.

The dissemination of numerous information broadcast services, gives rise to the scalability issue in wireless networks. Previous researchers have shown that the push-based data broadcast mechanism is efficient in reducing message traffic. However, most research efforts are dedicated to the dissemination of static information contents. In practice, information broadcast services can produce and deliver dynamic information contents. To efficiently convey the dynamic data, we device, in this paper, an adaptive information dissemination mechanism by exploiting the functionality of data broadcasting, to support the dissemination of static and dynamic information services simultaneously. In our design, both static and dynamic information services are subsumed as service groups, i.e. the building blocks with the uniform representation of structure and group popularity and, thus, the conventional scenario becomes a special case of our framework. Furthermore, in order to tolerate the broadcast traffic dynamics, we design an online load-based slot allocation and feedback control technique to deal with the adaptation of the service group classification, bandwidth allocation, and broadcast schedule so as to avoid performance degradation. It is shown by the experimental study that the proposed adaptive information dissemination mechanism associated with the online loan-based feedback control is able to achieve a substantial reduction of message traffic for dynamic dissemination in wireless networks.

With the proliferation of wireless information applications and services, data broadcasting known from radio networks has become an important mechanism for mobile data access in wireless communication networks. Two data delivery techniques, push and pull, and their hybrid combination bad been introduced to satisfy mobile user demands. However, most previous research efforts were elaborated upon the premise of a single channel with static workload and access pattern. The emergence of a multi-channel broadcast paradigm has attracted much research attention in the data broadcast community. We design an adaptive balanced scheme (ABS) which performs a heuristic search in pursuit of a fair balance of access time for a hybrid data delivery in a multi-channel data broadcast environment. The experimental results show that at the balanced point exploited by ABS, the result of channel partition and data item classification is very close to the optimal balance, and consequently, we are able to obtain the lowest mean access time in both push and pull channels simultaneously.

Data dissemination has significantly served as a scalable data delivery mechanism in wireless networks. However, even though the broadcast traffic has the nature of dynamic changes, most previous research efforts were elaborated upon the premise of static workloads and access patterns without having proper traffic awareness. In this paper, we address the existence of client impatience and accordingly devise an online traffic awareness mechanism based on a novel selective deferment and reflection (SDR) technique to estimate the dynamic workloads and access patterns in a granularity of a broadcast cycle. In comparison with prior probing and feedback approaches, our design is of practical usefulness in that it has low complexity and is light-weight without performance degradation. With various dynamic traffic scenarios, the experimental results show that with an increasing/decreasing workload, the real access frequency distribution is bounded by two specific estimated distributions. This fact in turn suggests us to employ a trigonometric tuning method to further enhance the estimation. In addition, we examine that the mean difference between the estimated access frequency distribution and the real one is very small, consequently indicating the feasibility and reliability of our proposed data broadcast mechanism with traffic awareness.

Active networks enable the customization of network functionality without the lengthy standard-mediated committee processes. Most of the works in the literature utilize capsules or active packets as the means to transfer code information across active networks. In this paper, we propose an active network infrastructure based on mobile agent technologies. In our prototype implementation, mobile agents are the building blocks carrying functional customizations, and the active nodes offer software application layers, caled agent servers, to process mobile agent-specific customizations to facilitate network functionality. Both integrated and discrete operational models of network customizations are supported. In addition, for the application-specific protocol development and deployment, an abstract protocol structure and a protocol loading mechanism are presented. Furthermore, we provide an agent management/control mechanism and devise a protocol management/control mechanism. As a result, improved network functionality can be achieved.

In relational databases, a composition requires three operations: join, projection and duplicate elimination. An expensive external sort is required to eliminate duplicates in a large file. Most conventional composition algorithms take join and duplicate elimination as separate operations to reduce overhead on either operation, but not both. Direct composition algorithms outperform all these algorithms by executing the composition as a single primitive. We show that the direct composition can even outperform its component operation, join, under various conditions. Moreover, when the density of the joining attribute is high enough, the hot spot direct composition may even run faster as the operand relations become bigger. These results can encourage real DBMSs to remove duplicates in some relational operations, and thus, to preserve the closure property of the relational data model.