Mica

The Berkeley Mica architecture is presented in [43] and it is a Commercial Off-The-Shelf (COTS) solution. It consumes 45mW in its active state and 30µW in its sleep state.

The core of the design is an Atmel ATmega128L 8-bit micro-controller, which has standard digital and analogue interfaces. There is also a radio transceiver, a co-processor

Technical Background

with external memory to handle wireless reprogramming, and a DC-DC converter to pro- vide a constant 3 Volts. It features 128Kb of instruction memory and 4Kb of data memory. There is also a 512Kb flash memory for measurement storage.

It does not have a separate processor for application and protocol processing but relies on an event based operating system and hardware accelerators to provide sufficient perfor- mance. The timing hardware accelerator catches the edge transition of the timing pulse. Once the timing information is captured, software then uses it to configure a serialization accelerator that automatically times and samples the individual bits. In receive mode, this accelerator provides a byte to the main datapath. In transmit mode, the micro-controller writes a byte at a time to this accelerator, which in effect acts like a type of Direct Mem- ory Access (DMA). It can operate up to 100 Kilobits per second (Kbps). Security can be implemented in software, on these devices, as there is no crypto accelerator.

2.2.2

Spec

Berkeley [45] have implemented a wireless sensor node minus the sensor system on an Application Specific Integrated Circuit (ASIC) using a 25µm process. It measures

2.5mm × 2.5mm, and it consumes 27mW in its active state and 2µW in its sleep state.

An architecture of the device is shown in Figure 2.2. It is estimated that this device would cost less than one dollar if manufactured in sufficient quantities.

It uses an 8-bit RISC CPU that has 16-bit instructions. It can operate at a frequency of 4Mhz. There is 3Kb of Static Random Access Memory (SRAM) organised into 512 bytes pages that can be mapped to the data or instruction memory by the address translation unit; this is so as to enable field programming of the device.

There is no baseband processor but computationally intensive operations of the RF communications, such as synchronisation and serialisation, are implemented as hardware accelerators. These accelerators can be used by the CPU to implement the radio protocol. There is no receiver in this device. It has a transmission rate of 50 Kbps.

Other components that are present are; the ADC for connecting to the sensor data, the analogue part of the RF sub-system, GPIO ports, system timers and an encryption

Figure 2.2:Architectural of Spec [45]

accelerator. Encryption is achieved using four 40-bit linear feedback shift registers that are XORed together to obtain a pseudo-random sequence. They are seeded by software and two devices would have to agree on this seed value before being able to communicate securely.

2.2.3

Intel Mote 2

Intel have developed a more powerful platform [2] that is designed for use with more intensive Digital Signal Processing (DSP) type operations, such as on video or acoustic data. It is a COTS implementation. In its active state it consumes less than 0.21W and in its deep sleep state it consumes 331µW .

It is designed around a XScale processor [48](PXA27x), which is a 32-bit device, and its operating frequency is in the range of 13 MHz to 416 MHz. It has a minimum of

512Kb of SRAM that can be augmented with a maximum of 32Mb of SDRAM. Flash

memory in the device ranges from 32M b to 64M b. For communication it uses a IEEE 802.15.4 (Zigbee) transceiver that can operate at 250 Kbps. The CPU has the standard

Technical Background

I/O interfaces integrated, such as I2C, SSP, UART, GPIO etc., but it also interfaces to audio and camera devices. There is an accelerator for multimedia operations. Security is implemented by using the Intel Wireless Trusted Platform which provides cryptographic engines for a full range of security primitives such as, random number generation, sym- metric and asymmetric cryptography, key exchange etc.

.

2.2.4

Conclusions

There is no one application scenario for WSN [22] and as such, the specification require- ments of the wireless sensor nodes will differ depending on their purpose. It has been argued [44] that the devices fall into four broad categories; specialised for low bandwidth sensing, generic low bandwidth sensing and communication relay, high bandwidth sens- ing, and a gateway device for connection to traditional networks. Spec is an example of a specialised device, Mica is a generic device and Intel mote 2 is used for high bandwidth sensing.

Energy is a key design constraint for all these categories of devices. Cost will be a bigger issue with specialised devices as they will be deployed in great numbers. This thesis targets the area of low bandwidth sensing devices that will have a fixed mission at deployment, similar to the Spec device.

Only the high bandwidth device, Intel Mote 2, has dedicated hardware support for asymmetric cryptography. The devices that are being targeted in this research either have no crypto engines, or rely on software to provide secure data transmission. They do not have accelerators for the asymmetric cryptosystem that is required to solve the key distribution problem in WSN. A key goal of this research to investigate a solution to this problem in an energy constrained device, and to verify and validate the proposed solution.

2.3

Network Establishment

The wireless sensor nodes must set up an ad-hoc network in order to send data back to the base station that extracts data from the network. How an individual wireless sensor node sends its data back to the base station depends on whether the base station is within range of the wireless sensor nodes, which is the scenario considered in [100]. If the wireless sensor nodes are capable of changing their transmission power output then there are three methods of data transfer.

1. Every wireless sensor node transfers its data independently to the base station. 2. Multi-hop networking. Intermediate wireless sensor nodes on the way to the base

station act as relays for the data. This can reduce energy, as the path loss term is proportional to distance squared. If the energy required by the transmitter in order for the receiver one metre away to receive the signal is 1nJ per bit, then the energy required to send this data 50m will be 50×50×1 or 2500nJ , but for five hops of 10m it will be 5×10×10×1 or 500nJ . Hence there is an eighty per cent improvement in energy efficiency using a multi hop method.

3. Cluster protocol. In this case, wireless sensor nodes in the same area have collab- orative data and so these elect a clusterhead that fuses the data before transferring it to the base station. The clusterhead has to do extra computation and also has an increased communication cost, because of this the clusters and clusterheads are changed dynamically.

The method used depends on the number of wireless sensor nodes and distance from the base station. It has been shown [100] that for distances greater than twenty metres the cluster protocol is the most efficient.

The discussion above assumes that the clusterhead wireless sensor node can transmit to the base station. When the wireless sensor node’s transmission range is limited, and the network is spread over a large geographical area where the individual wireless sensor nodes do not have access to the base station then a multi-hop networking protocol is required (see Figure 2.3). In this scenario the wireless sensor nodes in a particular region

Technical Background

collaborate to send their data to a clusterhead wireless sensor node that performs some DSP on the data before forwarding it in a multi-hop fashion to the base station.

Figure 2.3:Multi-hop network

In general the routing protocols can be divided into proactive and reactive routing paradigms [88]. For a proactive protocol, all the wireless sensor nodes store a routing table that enables them to communicate with every other wireless sensor node in the system. Any change in the topology would have to be communicated to every wireless sensor node so that it can update its routing table. In the case of reactive routing the network only finds routes as they are required. This approach is more suited to distributed wireless sensor networks, as it lends itself to low duty cycle radio transmission. Reactive routing can be either destination-initiated, where the data consumer (i.e. the base station) begins the route discovery process, or source-initiated, which is the opposite.

One such reactive routing scheme is directed diffusion [47]. This is a data centric protocol that uses a destination-initiated routing scheme. The scheme does not request data from a particular wireless sensor node but from a geographical area, hence a global unique addressing system is not required. The sink wireless sensor node (i.e. base station)

might ask a question such as, “what is the pH in a region X”. This request for data is known as an interest, and it takes the form of an attribute-value pairs, which in this case could be the pH level, geographical area and a data rate. The scheme has an exploratory phase and a reinforcement phase. In the first phase the data query is diffused (broadcast) through the network with a much reduced data rate in order to evaluate if the interest can be met. This flooding results in gradients being set up that specify the direction of the wireless sensor node from where the interest came from. If this first phase is successful then the data originator can reinforce a path that meets the user criteria of low latency and low energy. This reinforcement takes the form of increasing the data rate from the data source to the data sink. There can also be in-network data aggregation to improve the quality of the result.

In this thesis we assume that a network has been established. The form that the net- work takes is a multi-hop network where wireless sensor nodes can collaborate in cluster type formations before sending data back to the base station (see Figure 2.3). We fur- ther assume that a routing algorithm has been implemented that ensures a robust routing channel between two nodes in the network. Network establishment and routing protocols in wireless sensor networks are challenging and active areas of research but will not be considered further in this thesis.

2.4

Localisation

In order for the sensing measurements to be meaningful, and in order to derive the topol- ogy of the network, it is necessary for the wireless sensor nodes to know their global position. One method to do this would be to use GPS, but this would be a costly option in monetary terms. Another method is to use the radio signal to work out the distance of each wireless sensor node from some pre-determined points [87] called anchor wireless sensor nodes, and then triangulation can be used to work out each wireless sensor node’s global position.

A two-staged algorithm is proposed in [87]. Each of the wireless sensor nodes propa- gates a relative positional algorithm through the network. When the wireless sensor node

Technical Background

is in the relative positional map of four or more anchor wireless sensor nodes, it can com- pute its distance to each anchor device, and then through a process of triangulation it can work out its own position. It then enters the second stage and performs an iterative process of triangulation with its adjacent wireless sensor nodes until they agree on their position. A variation on this method is also proposed [86] that counts the number of hops and uses the range of each wireless sensor node to work out the distance, instead of calculating the absolute distance to the anchor devices. It is assumed that in this thesis that the wireless sensor nodes can run a localisation algorithm in order to determine their relative position using the techniques outlined above.

2.5

Energy Dissipation

Perhaps the most onerous challenge facing the emerging technology of wireless sensor networks is the requirement that the devices will have to operate for long periods in hostile or inaccessible environments where the replacement of the power source would not be possible. It is of course, for reasons of cost, impractical to replace the power source in a network containing hundreds or thousands of wireless sensor nodes even if they are accessible. For this reason it is imperative that the system architecture of the wireless sensor nodes and the network as a whole should be designed with the aim to minimising energy dissipation.

There are three sources of energy dissipation in a wireless sensor node; sensing cir- cuitry, digital computation and radio circuitry (see Figure 2.1). The sensing circuitry consists of the actual sensor itself and all the amplification and filtering components that are required. Digital computation is used to provide the DSP of the sensor data and for implementing the Open System Interconnect (OSI) protocol stack. Finally, the radio cir- cuitry consists of analogue electronics that are responsible for modulating / demodulating and actual transmission of the radio signal. This thesis is primarily concerned with min- imisation of digital computational energy.