Internet of Things Amr El Mougy Gina Maher

Data Collection in Sensor Networks

Data Collection from BLE Sensors BLE sensors are standalone. Data collection has to be done from each sensor individually Mainly two methods for BLE data collection: Smartphones (public sensing) Gateway Communication is typically one way Configuration parameters are allowed in the reverse direction

Comparison of Gateways and Smartphones Fixed data collection point  reliable Reliability depends on the smartphone density Medium cost Low cost Requires an additional device to configure the sensors Can send configuration commands from the phone Depending on the type, it can support WiFi, cellular connection, or both Always supports WiFi and cellular connections Always participates in the data collection Participation depends on the user’s willingness Dedicated for data collection Data collection competes with other applications Typically has stable power source Battery-powered

Structure of BLE Gateway Processor/Application Host API BLE Stack Profiles TCP/IP BLE Physical/Link Layers Ethernet Stack Cellular Stack WiFi Stack

Gateway Types Payload Extraction Packet Encapsulation (Data Pipe) BLE Attribute WiFi Packet Handle UUID Value WiFi - H IP-H TCP-H Value BLE Attribute Handle UUID Value WiFi Packet WiFi - H IP-H TCP-H Handle UUID Value

Data Collection from ZigBee Sensors Smartphones generally do not support ZigBee All ZigBee networks have a PAN coordinator where all data is collected From there the data can uploaded to the Internet Thus, the coordinator can act as a gateway

Why is Data Collection Challenging? Reliability, automation and fault-tolerance WSNs are required to remain operational for long durations without human intervention All nodes have dual function: sensing and relaying Transmissions typically consume the most energy Nodes go to low power (sleep) state to conserve energy This is called duty cycling. Low duty cycles mean nodes remain in sleep state for longer durations to conserve energy Generated data needs a path to the PAN coordinator Sleeping nodes will not participate in forwarding  need to plan sleep schedules carefully Sensor data is many-to-one. Control data is one-to-many Control traffic is minor but often more critical. Reliability is often imposed Energy efficiency is at the core of all challenges!!

The Three Stages of Data Collection Node Deployment Control Message Disseminat-ion Data Delivery Area Coverage Location Coverage Flooding-Based Gossiping-Based QoS Metrics (Latency, reliability, energy)

The Node Deployment Problem Problem Statement: What is the optimum placement of sensor nodes in order to satisfy the requirements of a certain application? This placement needs to ensure that all required attributes are sensed and all nodes have a path to the PAN coordinator (the coverage and connectivity problems) Sensors are modeled as a circular disk with sensing range Rs metres and communication range Rc metres Rc Rs is determined by the sensing hardware while Rc is determined by transceiver Rs

The Node Deployment Problem Nodes go into sleep mode periodically and their batteries die Conclusion  not all deployed nodes may be active at all time Thus, redundancy is required in the network (deploy more nodes than needed) The problem now is known as the k-coverage and k-connectivity problems: every point must be covered by at least k sensors every sensor must have a path to the gateway even if k – 1 paths fail

1-coverage, 1-connectivity Examples 1-coverage, 1-connectivity Strip pattern used as building block for 1-coverage and 1-connectivity network ** F. Wang and J. Liu, “Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches,” IEEE Communication Surveys and Tutorials, Vol. 13, No. 4, 2011.

Node Deployment for Area Coverage Goal is to cover every point in the area with at least k sensors Research has discovered the following pattern to be the universal element pattern for 1-coverage and k-connectivity (k ≤ 6) Where d1 and d2 are parameters that depend on Rc and Rs ** F. Wang and J. Liu, “Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches,” IEEE Communication Surveys and Tutorials, Vol. 13, No. 4, 2011.

Examples ** F. Wang and J. Liu, “Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches,” IEEE Communication Surveys and Tutorials, Vol. 13, No. 4, 2011.

** F. Wang and J. Liu, “Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches,” IEEE Communication Surveys and Tutorials, Vol. 13, No. 4, 2011.

Node Deployment for Location Coverage Goal is to cover specific points in an area with at least k sensors Coverage is straightforward and is typically manual These locations may be sparse, thus requiring relay nodes The challenge is to deploy relay nodes to ensure connectivity More that one relay node may be needed to ensure k connectivity ** F. Wang and J. Liu, “Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches,” IEEE Communication Surveys and Tutorials, Vol. 13, No. 4, 2011.

Node Deployment for Location Coverage Start with the deployed sensors Add a relay node with three edges at the intersection of the medians Check the lengths of each edge. If greater than communication range, add a relay

Traffic-Aware Node Deployment The preceding algorithm does not consider traffic Nodes close to the base station relay more traffic Needs to consider the nature of traffic so as not to over-consume the batteries of certain relay nodes Example: how to optimally distribute N relay nodes if you have 2 sources S1 producing 60% of the data and S2 producing 40% of the data: S1 S2 S1 S2 1 3 N 1 3 N 1 3 N 1 3 N - Δ V V 1 3 N + Δ 1 3 N S0 S0

Data Delivery After the network is deployed, sensor data has to be relayed to the base station The network is usually viewed as a tree, rooted at the base station The tree is either manually defined or autonomously discovered If autonomously discovered, the process is either central (by the base station), or distributed (every sensor node discovers the topology on its own) Nodes also go to sleep cycles and eventually die out Data may be lost due to interference or collisions ** F. Wang and J. Liu, “Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches,” IEEE Communication Surveys and Tutorials, Vol. 13, No. 4, 2011.

Data Delivery Models Event-driven: data is generated in response to an event. Data from several sensors may be highly correlated. Fusion techniques often employed Query-driven: network is interactive. Only sends data on demand Continuous-based: real-time data. Network is always sending data Time-driven: data is collected periodically from the environment Transmitted data may be loss-tolerant or not Internet routing protocols are not suitable for sensor networks since they do not consider energy efficiency

Data Delivery Functions Two main modules: Topology maintenance: construct topology that guarantees coverage and connectivity while considering network dynamics Transmission scheduler: determines when packet transmissions should take place to reduce collisions, ensure energy efficiency and consider QoS constraints ** F. Wang and J. Liu, “Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches,” IEEE Communication Surveys and Tutorials, Vol. 13, No. 4, 2011.

Topology Maintenance Two main parameters to control: Transmit power: to maintain a balance between connectivity and energy efficiency (out of our scope) Duty cycles: to put the sensors in sleep mode Challenges in duty cycle management include: End-to-end delay: nodes waiting for other nodes to wake up Collision rates: shortening active cycles means more nodes trying to transmit at the same time Control overhead: mainly for synchronization ** R. Carrano, D. Passos, L. Magalhaes, and C. Albuquerque, “Survey and Taxonomy of Duty Cycling Mechanisms in Wireless Sensor Networks,” IEEE Communication Surveys and Tutorials, Vol. 16, No. 1, 2014.

Synchronous Duty Cycling All nodes keep common time reference. Not necessarily a global time Synchronization information is exchanged throughout the network to keep a certain degree of alignment Overhead increases significantly with the number of nodes Rendezvous: strict synchronization. Challenging to maintain for large networks. Suffers from data forwarding interruption (nodes go to sleep, unaware that a frame is coming Skewered: deals with data forwarding interruption. Typically a tree is formed and a schedule is set. Needs topology discovery and maintenance ** R. Carrano, D. Passos, L. Magalhaes, and C. Albuquerque, “Survey and Taxonomy of Duty Cycling Mechanisms in Wireless Sensor Networks,” IEEE Communication Surveys and Tutorials, Vol. 16, No. 1, 2014.

Semi-Synchronous Duty Cycling Nodes are clustered and the clusters are synchronized Inter cluster communication is asynchronous Main challenge is choosing the cluster head Two ways of achieving this: Spontaneous: loose association. Only timestamps are exchanged. Not efficient, nodes may belong to many clusters Election-based: nodes communicate to elect a manager periodically (typically node with highest remaining power). Extra overhead needed ** R. Carrano, D. Passos, L. Magalhaes, and C. Albuquerque, “Survey and Taxonomy of Duty Cycling Mechanisms in Wireless Sensor Networks,” IEEE Communication Surveys and Tutorials, Vol. 16, No. 1, 2014.

Asynchronous Duty Cycling Preamble: sending nodes transmit a preamble frame that is longer than any active/sleep cycle. Nodes wakeup periodically to check for this frame. If not found they sleep right away Receiver-initiated: nodes periodically wake up and send a beacon, indicating willingness to receive. Senders send an ACK if they have a packet to send On demand: relies on the availability of a separate radio that can trigger wake up calls. The second radio should consume less than a tenth of the energy of the primary radio ** R. Carrano, D. Passos, L. Magalhaes, and C. Albuquerque, “Survey and Taxonomy of Duty Cycling Mechanisms in Wireless Sensor Networks,” IEEE Communication Surveys and Tutorials, Vol. 16, No. 1, 2014.

Asynchronous Duty Cycling Random: works in highly dense networks. Nodes go to sleep for a period that is proportional to the number of neighbors Schedule-based: sender and receiver schedule wake up slots such that they overlap in at least one slot. Duty cycles here are typically high ** R. Carrano, D. Passos, L. Magalhaes, and C. Albuquerque, “Survey and Taxonomy of Duty Cycling Mechanisms in Wireless Sensor Networks,” IEEE Communication Surveys and Tutorials, Vol. 16, No. 1, 2014.

Reliability In some applications, reliable packet delivery is required Traditionally, this is done using sequence numbers and receiver feedback Hop-by-hop recovery notices missing sequence numbers and sends feedback asking for retransmissions High packet loss can also occur due to changing network dynamics. In this case end-to-end recovery is also needed at the base station The base station notices many sequence numbers missing and may instruct the topology maintenance module to react ** F. Wang and J. Liu, “Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches,” IEEE Communication Surveys and Tutorials, Vol. 13, No. 4, 2011.

Latency A node’s transceiver consumes a decreasing amount of energy for the following states: transmission, reception or idle, and sleep Thus a node sending to its neighbor may waste time and energy waiting for it to wake up If sleep cycles are not considered, high packet loss may be incurred Also need to maximize sleep cycles Over a path, it is optimal for nodes to wake up only when they need to send/receive, and then go right back to sleep ** F. Wang and J. Liu, “Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches,” IEEE Communication Surveys and Tutorials, Vol. 13, No. 4, 2011.

DMAC Sensors on a path will wake up, receive, send, then sleep A sensor sets More Data Flag to 1 if it wishes its receives to turn back to receiving mode after transmitting, instead of going back to sleep A receiver connected to two senders will predict that there are more packets to be received, and will return to receiving after transmission Nodes use slotted CSMA. After each slot, senders can set the More to send flag to 1 to indicate that there is a retransmission ** F. Wang and J. Liu, “Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches,” IEEE Communication Surveys and Tutorials, Vol. 13, No. 4, 2011.

Throughput Nodes near the base station are the bottleneck of the network in terms of throughput and energy consumption Some authors suggest dynamic placement of nodes. Thus, k coverage and connectivity are not uniform throughout the network Funneling-MAC divides the network into three regions: high intensity, low intensity, and medium intensity TDMA and more redundancy are used in the high intensity region CSMA is used as we move away from the sink ** F. Wang and J. Liu, “Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches,” IEEE Communication Surveys and Tutorials, Vol. 13, No. 4, 2011.

Control Message Dissemination Control messages come from the sink. Thus, it is a one-to-many communication Even though these are relatively a small number of packets, they are critical to network performance Two main approaches: Flooding: each node forwards any control packet it receives until reaches the maximum hop count. Many duplicates and redundancies occur. High energy consumption Gossiping: every node forwards the message but only according to some predefined probability. If properly defined, there is a high chance that the whole network is covered. Packet loss may lead to some nodes missing important messages