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Due to limited energy supply on many Internet of Things (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Flooding is a critical way to disseminate messages through the whole network. Capture effect enabled concurrent broadcast is appealing to accelerate network flooding in asynchronous duty cycle networks. However, when the flooding payload’s size is large, the concurrent broadcast performance is far from efficient due to the frequently unsatisfied capture effect. Intuitively, senders can send a short packet containing partial flooding payload to keep concurrent broadcast efficiency. In practice, we still face two challenges. Considering packet loss, a receiver needs an effective way to recover the entire flooding payload from several received packets as soon as possible. Moreover, considering different channel states of different senders, how a sender chooses the optimal packet length to guarantee high channel utilization is not easy. In this paper, we propose Chase++ a Fountain-code based concurrent broadcast control layer to enable fast flooding in asynchronous duty cycle networks. Chase++ uses Fountain code to alleviate the negative influence of a certain part of the flooding payload’s continuous loss. Moreover, Chase++ adaptively selects packet length with the local estimation of channel utilization. Specifically, Chase++ partitions long payload into several short payload blocks, further encoded into many encoded payload blocks by Fountain-code. Then, with temporal and spatial features of the sampled RSS (received signal strength) sequence, a sender estimates the number of concurrent senders. Finally, according to the estimated number of concurrent senders, the sender determines the optimal number of encoded payload blocks in a packet and assembles the encoded payload blocks as lots of packets. Then, the concurrent broadcast layer continuously transmits these packets. Receivers can recover the original flooding payload after several independent encoded payload blocks are collected. We implement Chase++ in TinyOS with TelosB nodes. We further evaluate Chase++ on Local testbed with 50 nodes and Indriya testbed with 95 nodes. The improvement of network flooding speed can reach 23.6% and 13.4%, respectively. [ABSTRACT FROM AUTHOR]

In large-scale industrial wireless sensor networks (IWSNs), retransmission strategies are widely adopted to guarantee the reliability of multihop forwarding. However, keeping retransmitting over a lossy link may consecutively fail. Moreover, the retransmission will also be useless over the back-up links that are spatially correlated with the previously failed link. Thus, it is necessary to design a unified retransmission strategy by considering both temporal and spatial link properties to further improve data delivery performance in IWSNs. In this paper, we propose RxLayer, a practical and general supporting layer for data retransmission. By using conditional probability models, RxLayer captures the temporal and spatial link properties without inducing noticeable overhead. According to RxLayer, data forwarding protocol could assess the success rate of the next transmission over the currently used link. When the currently used link is seriously degraded, RxLayer will select an optimal back-up link that temporally has the best link quality to retransmit. RxLayer can be transparently integrated with existing data forwarding protocols. The prototype of RxLayer is implemented in TinyOS. We have evaluated its performance on both indoor and outdoor testbeds. Experimental results show that RxLayer can improve data delivery reliability and energy efficiency in various scenarios compared with the state-of-the-art retransmission strategy. On average, data delivery performance is improved by up to 7.96 ${\%}$ , and the number of transmissions is reduced by 43.6%. [ABSTRACT FROM AUTHOR]

Asynchronous duty cycle is widely used for energy constraint wireless nodes to save energy. The basic flooding service in asynchronous duty cycle networks, however, is still far from efficient due to severe packet collisions and contentions. We present Chase, an efficient and fully distributed concurrent broadcast layer for flooding in asynchronous duty cycle networks. The main idea of Chase is to meet the strict signal time and strength requirements (e.g., Capture Effect) for concurrent broadcast while reducing contentions and collisions. We propose a distributed random inter-preamble packet interval adjustment approach to constructively satisfy the requirements. Even when requirements cannot be satisfied due to physical constraints (e.g., the difference of signal strength is less than a 3 dB), we propose a lightweight signal pattern recognition-based approach to identify such a circumstance and extend radio-on time for packet delivery. We implement Chase in TinyOS with TelosB nodes and extensively evaluate its performance. The implementation does not have any specific requirement on the hardware and can be easily extended to other platforms. The evaluation results also show that Chase can significantly improve flooding efficiency in asynchronous duty cycle networks. [ABSTRACT FROM PUBLISHER]

Packet corruption caused by collision is a critical problem that hurts the performance of wireless networks. Conventional medium access control (MAC) protocols resort to collision avoidance to maintain acceptable efficiency of channel utilization. According to our investigation and observation, however, collision avoidance comes at the cost of miscellaneous overhead, which oppositely hurts channel utilization, not to mention the poor resiliency and performance of those protocols in face of dense networks or intensive traffic. Discovering the ability to tolerate collisions at the physical layer implementations of wireless networks, we in this paper propose $Coco$ , a protocol that advocates simultaneous accesses from multiple senders to a shared channel, i.e., optimistically allowing collisions instead of simply avoiding them. With a simple but effective design, Coco$ addresses the key challenges in achieving collision tolerance, such as precise sender alignment and the control of transmission concurrency. We implement Coco$ in 802.15.4 networks and evaluate its performance through extensive experiments with 21 TelosB nodes. The results demonstrate that Coco in general cases, compared with state-of-the-arts protocols. [ABSTRACT FROM AUTHOR]

Delay is an important metric to understand and improve system performance. While existing approaches focus on aggregated delay statistics in pre-programmed granularity and provide results such as average and deviation, those approaches may not provide fine-grained delay measurement and thus may miss important delay characteristics. For example, delay anomaly, which is a critical system performance indicator, may not be captured by coarse-grained approaches. We propose a new measurement structure design called order preserving aggregator (OPA). Based on OPA, we can efficiently encode and recover the ordering and loss information by exploiting inherent data characteristics. We then propose a two-layer design to convey both ordering and time stamp, and efficiently derive per-packet delay/loss measurement. We evaluate our approach both analytically and experimentally. The results show that our approach can achieve per-packet delay measurement with an average of per-packet relative error at 2%, and an average of aggregated relative error at 10^-5, while introducing additional communication overhead in the order of 10^-4 in terms of number of packets. While at a low data rate, the computation overhead of OPA is acceptable. Reducing the computation and communication overhead under high data rate, to make OPA more practical in real applications, will be our future direction. [ABSTRACT FROM PUBLISHER]

The notion of global time is of great importance for many sensor network applications. Time reconstruction methods aim to reconstruct the global time with respect to a reference clock. To achieve microsecond accuracy, MAC-layer timestamping is required for recording packet transmission and reception times. The timestamps, however, can be invalid due to multiple reasons, such as imperfect system designs, wireless corruptions, or timing attacks, etc. In this paper, we propose ART, an accurate and robust time reconstruction approach to detecting invalid timestamps and recovering the needed information. ART is much more accurate and robust than threshold-based approach, especially in dynamic networks with inherently varying propagation delays. We evaluate our approach in both testbed and a real-world deployment. Results show that: 1) ART achieves a high detection accuracy with low false-positive rate and low false-negative rate; 2) ART achieves a high recovery accuracy of less than 2 ms on average, much more accurate than previously reported results. [ABSTRACT FROM PUBLISHER]

To guarantee reliability, bulk data dissemination relies on the negotiation scheme in which senders and receivers negotiate transmission schedule through a three-way handshake procedure. However, we find negotiation incurs a long dissemination time and seriously defers the network-wide convergence. On the other hand, the flooding approach, which is conventionally considered inefficient and energy-consuming, can facilitate bulk data dissemination if appropriately incorporated. This motivates us to pursue a delicate tradeoff between negotiation and flooding in the bulk data dissemination. We propose SurF (Survival of the Fittest), a bulk data dissemination protocol which adaptively adopts negotiation and leverages flooding opportunistically. SurF incorporates a time-reliability model to estimate the time efficiencies (flooding versus negotiation) and dynamically selects the fittest one to facilitate the dissemination process. We implement SurF in TinyOS 2.1.1 and evaluate its performance with 40 TelosB nodes. The results show that SurF, while retaining the dissemination reliability, reduces the dissemination time by 40 percent in average, compared with the state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

Opportunistic routing, offering relatively efficient and adaptive forwarding in low-duty-cycled sensor networks, generally allows multiple nodes to forward the same packet simultaneously, especially in networks with intensive traffic. Uncoordinated transmissions often incur a number of duplicate packets, which are further forwarded in the network, occupy the limited network resource, and hinder the packet delivery performance. Existing solutions to this issue, e.g., overhearing or coordination based approaches, either cannot scale up with the system size, or suffer high control overhead. We present Duplicate-Detectable Opportunistic Forwarding (DOF), a duplicate-free opportunistic forwarding protocol for low-duty-cycled wireless sensor networks. DOF enables senders to obtain the information of all potential forwarders via a slotted acknowledgment scheme, so the data packets can be sent to the deterministic next-hop forwarder. Based on light-weight coordination, DOF explores the opportunities as many as possible and removes duplicate packets from the forwarding process. We implement DOF and evaluate its performance on an indoor testbed with 20 TelosB nodes. The experimental results show that DOF reduces the average duplicate ratio by 90%, compared to state-of-the-art opportunistic protocols, and achieves 61.5% enhancement in network yield and 51.4% saving in energy consumption. [ABSTRACT FROM AUTHOR]

We present a comprehensive delay performance measurement and analysis in a large-scale wireless sensor network. We build a lightweight delay measurement system and present a robust method to calculate the per-packet delay. We show that the method can identify incorrect delays and recover them with a bounded error. Through analysis of delay and other system metrics, we seek to answer the following fundamental questions: What are the spatial and temporal characteristics of delay performance in a real network? What are the most important impacting factors, and is there any practical model to capture those factors? What are the implications to protocol designs? In this paper, we identify important factors from the data trace and show that the important factors are not necessarily the same with those in the Internet. Furthermore, we propose a delay model to capture those factors. We revisit several prevalent protocol designs such as Collection Tree Protocol, opportunistic routing, and Dynamic Switching-based Forwarding and show that our model and analysis are useful to practical protocol designs. [ABSTRACT FROM PUBLISHER]

Sensor networks are deemed suitable for large-scale deployments in the wild for a variety of applications. In spite of the remarkable efforts the community put to build the sensor systems, an essential question still remains unclear at the system level, motivating us to explore the answer from a point of real-world deployment view. Does the wireless sensor network really scale? We present findings from a large-scale operating sensor network system, GreenOrbs, with up to 330 nodes deployed in the forest. We instrument such an operating network throughout the protocol stack and present observations across layers in the network. Based on our findings from the system measurement, we propose and make initial efforts to validate three conjectures that give potential guidelines for future designs of large-scale sensor networks. 1) A small portion of nodes bottlenecks the entire network, and most of the existing network indicators may not accurately capture them. 2) The network dynamics mainly come from the inherent concurrency of network operations instead of environment changes. 3) The environment, although the dynamics are not as significant as we assumed, has an unpredictable impact on the sensor network. We suggest that an event-based routing structure can be trained and thus better adapted to the wild environment when building a large-scale sensor network. [ABSTRACT FROM AUTHOR]

We study the ubiquitous data collection for mobile users in wireless sensor networks. People with handheld devices can easily interact with the network and collect data. We propose a novel approach for mobile users to collect the network-wide data. The routing structure of data collection is additively updated with the movement of the mobile user. With this approach, we only perform a limited modification to update the routing structure while the routing performance is bounded and controlled compared to the optimal performance. The proposed protocol is easy to implement. Our analysis shows that the proposed approach is scalable in maintenance overheads, performs efficiently in the routing performance, and provides continuous data delivery during the user movement. We implement the proposed protocol in a prototype system and test its feasibility and applicability by a 49-node testbed. We further conduct extensive simulations to examine the efficiency and scalability of our protocol with varied network settings. [ABSTRACT FROM AUTHOR]