Expected Delivery Delay Research Articles

Smart stochastic routing for 6G-enabled massive Internet of Things

Faster and energy-efficient data transmission is desired for massive Internet of Things (IoT) applications in sixth-generation networks. In such high speed networks, providing reliable data delivery with low delay, while maintaining energy-efficiency, is a challenging task. In this paper, a deep learning-based stochastic routing approach, called smart stochastic routing (SSR), is presented to address this challenge. SSR takes into account reliability, delays due to transmission, reception and processing of the neighbors’ information, and energy consumption and remaining energy of IoT devices. Through our proposed mathematical model, a dataset is generated to train a deep neural network, which predicts the best routing path from source to destination and achieves substantial accuracy over the mathematically generated dataset. Through simulations, we show the efficacy of SSR over conventional stochastic routing in terms of reduced energy consumption and expected delivery delay.

UAV-Aided Data Delivery Scheme Based on Opportunistic Virtual Intersections for Smart Transportation Networks

Smart transportation networks, featured by high mobility and intermittent connectivity, are facing critical challenges in data delivery especially when the network is sparse and with less or no support from any infrastructure, such as in disaster and military environments. Unmanned aerial vehicles (UAVs), due to their flexible mobility, can be envisioned to enhance the connectivity of ground vehicular networks. In this paper, incorporating UAVs into vehicular networks, a novel data delivery scheme, named UAV-assisted data delivery (UADD), is proposed for smart transportation networks. We introduce a novel concept called “opportunistic virtual intersections”, at which UAVs can communicate with ground vehicles, and assist to deliver data packets. Specifically, at an opportunistic virtual intersection, UAVs may act as a relay, which can relay data packets between two vehicles as an intermediate hop, or deliver data packets in a store-carry-and-forward manner. The data forwarding rules are devised based on expected delivery delay and delivery probability of data packets. The performance evaluation demonstrates that the proposed UAV-assisted data delivery scheme can achieve a significant improvement of data delivery performance in terms of delivery delay and delivery ratio.

A Data Forwarding Approach for Fire-Rescue Scenario with Multi-Type Mobile Nodes

The opportunistic mobile sensor network has been extensively applied in various public safety applications such as the fire rescue and earthquake rescue, since it can provide a surveillance range with an inexpensive cost and avoid the dangers of humans staying in risk zones. However, due to some environmental events such as building structure damage, airflow push, and fire explosions, the sensor nodes sprinkled into the fire-rescue scenario may be kept moving. Thus, the contacts between nodes become momentary, and the data packets cannot be forwarded along stable communication paths. To this end, the opportunistic forwarding manner is adopted in the fire-rescue scenario to enable the data packets to be transferred to the rescue control center (RCC) through some discrete hops. The contributions of this paper are threefold. First, the nodes in the fire-rescue scenario are carefully investigated and classified into four types: small-range mobile nodes (SRNs), large-range mobile nodes (LRNs), firefighter nodes (FNs), and robot nodes (RNs). Second, we formulate the data forwarding problem, and the optimal proportions of SRNs, LRNs, and FNs in data holders are mathematically analyzed to obtain the maximum delivery ratio. Third, a data forwarding approach for fire-rescue scenario (DFAFR) is proposed. In DFAFR, the optimal proportions of SRNs, LRNs, and FNs in data holders are maintained as far as possible through selecting different types of data holder candidates, and then the new data holders are determined from these data holder candidates and the adjacent RNs on basis of their expected delivery delay. Finally, the performance of DFAFR is analyzed through simulations of the fire-rescue scenario, and the results indicate that DFAFR can enhance the delivery ratio and shorten the delivery delay while the forwarding overhead is restricted.

Delay-Constrained Routing via Heterogeneous Vehicular Communications in Software Defined BusNet

As a critical part of public transportation systems, buses driving routinely in urban areas naturally form a bus ad-hoc network (BusNet). Different from conventional vehicular ad-hoc networks, the sparse distribution of buses magnifies the problem of intermittent network connectivity, making it more challenging for real-time routing. In this paper, we first present a novel architecture called software-defined BusNet, where the distributed controllers (i.e., base stations) collect traffic information and real-time requests from buses via vehicle-to-cellular communication. The collected information enables the logically centralized controller to make routing decisions with global traffic information. Then, the routing decisions are returned to roadside units (RSUs) and buses, which will instruct the data broadcast from RSUs to buses via vehicle-to-roadside (V2R) communication, and data forwarding among the buses through vehicle-to-vehicle (V2V) communication. On this basis, we formulate the problem of delay-constrained routing for BusNet (DCRB) which aims to maximize both the delivery ratio and the bandwidth efficiency. We build a probabilistic model to analyze the encounter probability and encounter delay between the relay buses based on the predicable bus traces. Further, we derive the expected reachability and expected delivery delay of the multi-hop routing path. The routing graph is constructed for searching the feasible routing paths via the modified Dijkstra algorithm. Four routing selection strategies are proposed based on different metrics: HRF (highest reachability first), SDF (shortest delivery delay first), MHF (minimum number of hops first), and RDH (combination of reachability, delay, and hop). Finally, we build the simulation model and implement the proposed routing algorithms for performance evaluation. The comprehensive simulation results demonstrate that RDH achieves the best overall performance on improving system delivery ratio and enhancing bandwidth efficiency.

Joint Roadside Unit Deployment and Service Task Assignment for Internet of Vehicles (IoV)

Internet of Vehicles (IoV) is a promising Internet of Things application, where roadside unit (RSU) plays an important role for network service provisioning. How to select the number and locations of RSUs to deploy and allocate the traffic load to them is a critical and practical open problem. Most of the existing work focused on 1-D scenarios assuming unlimited RSU capacity, while a more practical 2-D case with limited RSU capacity has not been fully considered yet. In this paper, we investigate an RSU deployment problem for 2-D IoV networks considering the expected delivery delay requirements and task assignment. We formulate a novel utility-based maximization problem to solve the RSU deployment problem, where the utility function indicates the total benefit from the RSU deployment. We observe that each RSU has an irregular service area, which makes the problem much more difficult than the traditional facility location problem. Then, we design a utility-based RSU deployment algorithm (URDA), a linear programming-based clustering algorithm, to solve the problem. The gap between URDA and the optimal solution has been analyzed, which proved that the proposed URDA is near optimal if the deployment cost is low. Extensive simulations have been conducted to demonstrate the effectiveness and superiority of the proposed solution for IoV network service guarantee over other approaches.

Delay-constrained flooding in extremely low-duty-cycle sensor networks

Flooding is a fundamental function for the network-wide dissemination of command, query, and code update in wireless sensor networks. However, it is challenging to enable fast and energy-efficient flooding in sensor networks with low-duty cycles because it is rare that multiple neighboring nodes wake up at the same time, making broadcast instinct of wireless radio unavailable. The unreliability of wireless links deteriorates the situation. In this work, we study the delay-constrained flooding problem in order to disseminate data packets to all nodes within given expected delivery delay. In particular, a transmission power control–based flooding algorithm is proposed to reduce the flooding delay in such low-duty-cycle sensor networks. According to the soft delay bound, each node can locally adjust its transmission power level. To alleviate transmission conflicts, the backoff method with transmission power adaptive mechanism has been proposed. Based on the large-scale simulations, we validate that our design can reduce flooding delay with small extra energy expenditure compared with conventional flooding schemes.

An Application-Oriented Buffer Management Strategy in Opportunistic Networks

In Opportunistic networks (ONs), buffer management is critical to improve the message exchanging efficiency due to the limited storage space and transmission bandwidth at the wireless edge. Current solutions make message scheduling and drop policy based on assumptions that messages can always been forwarded in a single contact, and all node pairs have the same contact rates. However, such ideal assumptions are invalid for realistic mobility traces of hand-held. Recent studies show that the single contact duration is limited and the mobility of nodes is heterogeneous in reality. In this paper, a buffer management strategy based on contact duration and heterogeneous mobility is proposed to improve the efficiency of buffer policy in the practical applications. We mainly focus on the minimization of the total expected delivery delay for all messages in ONs with resource constraints. Using the global network information including existing copies of message in the network, the distribution of pair-wise inter-contact time and contact duration between nodes, we develop a function to compute per-message utility which reflects the contribution of single message to the total expected delivery delay. Messages are scheduled or dropped according to their utilities. Simulation results show that our proposed strategy not only achieves lower delivery delay than mainstream strategies, but also keeps a high delivery ratio and a low network overhead.

Setting strategy of delay-optimization-oriented SMAC contention window size.

Some frame components, such as SYNC (frame synchronization) and RTS/CTS (Ready to Send/Clear to Send), are not taken into consideration when the traditional setting strategies conduct the optimization of SMAC (Sensor MAC) contention window size. This paper proposes mathematical models that allow the analysis of data packets forwarding delay within one SMAC virtual cluster. Simulation results in OMNeT++ show good agreements with the proposed mathematical models, validating the models’ correctness. The curve analyses of the models confirm the existence of delay-optimization-oriented contention window size that is closely related to the number of simultaneously contending nodes. Afterwards, it is shown that SYNC, RTS/CTS and EIFS (Extended InterFrame Space) have impacts on the optimal contention window size and expected delivery delay to various degrees, as well as throughput and energy efficiency. One ideal setting strategy of delay-optimization-oriented SMAC contention window size requires the combination of the network scale, SYNC, RTS/CTS and EIFS. Additionally, it is demonstrated that the proposed setting strategy makes contributions to the improvement in the existing SMAC extensions when they are integrated with each other, in terms of the end-to-end delay, throughput and energy consumption.

Home-Based Zero-Knowledge Multi-Copy Routing in Mobile Social Networks

A mobile social network (MSN) is a special kind of delay tolerant network (DTN) composed of mobile nodes that move around and share information with each other through their carried short-distance wireless communication devices. A main characteristic of MSNs is that mobile nodes in the networks generally visit some locations (namely, community homes) frequently, while visiting other locations less frequently. In this paper, we propose a novel zero-knowledge multi-copy routing algorithm, homing spread (HS), for homogeneous MSNs, in which all mobile nodes share all community homes. HS is a distributed and localized algorithm. It mainly lets community homes spread messages with a higher priority. Theoretical analysis shows that HS can spread a given number of message copies in an optimal way when the inter-meeting time between any two nodes and between a node and a community home follows independent and identical exponential distributions, respectively. We also extend HS to the heterogeneous MSNs, where mobile nodes have different community homes. In addition, we calculate the expected delivery delay of HS, and conduct extensive simulations. Results show that community homes are important factors in message spreading. By using homes to spread messages faster, HS achieves a better performance than existing zero-knowledge MSN routing algorithms, including Epidemic (with a given number of copies), and Spray&Wait.

Reliable Bulk-Data Dissemination in Delay Tolerant Networks

Delay/Disruption Tolerant Network (DTN) differs from the conventional networks in that it has no continuous or contemporaneous connections among wireless nodes. Its inherent characteristic of intermittent connections makes existing routing solutions hardly to be applied directly. Epidemic routing using random linear network coding has been studied and proved as an efficient way for delivering small amount of data. To our best knowledge, we are the first to study high performance reliable transmission for bulk or stream-like data in DTNs. In this paper, we propose a dynamic segmented network coding scheme to efficiently exploit the transmission opportunity that is scarce in DTNs. In particular, we adopt a dynamic segment size control mechanism, which makes the segmentation adapt to the dynamics of the network. A lower bound of the expected delivery delay for bulk-data dissemination using segmented network coding is also derived. Both analytical and simulation results validate the high performance of our proposal. Several other interesting findings are also observed.

Performance Modeling for Relay Cooperation in Delay Tolerant Networks

Delay tolerant networks (DTNs) rely on the mobility of nodes and sequences of their contacts to compensate for lack of continuous connectivity and thus enable messages to be delivered from end to end in a "store-carry-forward" way, where multiple relay nodes are usually employed in the message delivery process. In this paper, we focus on such relay cooperation and analytically explore its impact on the delivery performance in DTNs. Specifically, we first develop a continuous time Markov chain-based theoretical framework to model the complicated message delivery process in delay tolerant networks adopting the two-hop relay algorithm. We then derive closed-form expressions for both the expected delivery delay and the corresponding expected delivery cost, where the important relay behaviors of forwarding traffic for itself or for other nodes are carefully incorporated into the analysis.

Trajectory-Based Data Forwarding for Light-Traffic Vehicular Ad Hoc Networks

This paper proposes a Trajectory-Based Data (TBD) Forwarding scheme, tailored for the data forwarding for roadside reports in light-traffic vehicular ad hoc networks. State-of-the-art schemes have demonstrated the effectiveness of their data forwarding strategies by exploiting known vehicular traffic statistics (e.g., densities and speeds). These results are encouraging, however, further improvements can be made by taking advantage of the growing popularity of GPS-based navigation systems. This paper presents the first attempt to effectively utilize vehicles' trajectory information in a privacy-preserving manner. In our design, such trajectory information is combined with the vehicular traffic statistics for a better performance. In a distributed way, each individual vehicle computes its end-to-end expected delivery delay to the Internet access points based on its position on its vehicle trajectory and exchanges this delay with neighboring vehicles to determine the best next-hop vehicle. For the accurate end-to-end delay computation, this paper also proposes a link delay model to estimate the packet forwarding delay on a road segment. Through theoretical analysis and extensive simulation, it is shown that our link delay model provides the accurate link delay estimation and our forwarding design outperforms the existing scheme in terms of both the data delivery delay and packet delivery ratio.

Stochastic Models of Internal Mail Delivery Systems

This paper develops two stochastic models of an internal mail delivery system in which a single clerk picks up, sorts and delivers mail to a closed loop of offices. The two models differ in whether deliveries are made at scheduled times or not. For a model in which all mail picked up each round is sorted before the next delivery, we assume that mail is generated in the system by a stationary Poisson process and derive an expression for the expected delay between generation of a letter and its ultimate delivery. These results are then extended to systems in which letters are generated according to a stationary compound Poisson process and to multiple clerk delivery systems. A second model in which mail is delivered at scheduled times only is shown to be equivalent to a classical storage process. For this model, we derive bounds on the expected number of letters left unsorted at the start of a scheduled delivery and the expected delivery delay. This model is also generalized to multiclerk systems.